Exposure to Per- and Polyfluoroalkyl Substances and Breast Cancer Risk: A Systematic Review and Meta-analysis of Epidemiologic Studies.

We synthesized the epidemiologic evidence on the associations between per- and polyfluoroalkyl substances (PFAS) exposure and breast cancer risk. Our systematic review and meta-analysis included 18 and 11 articles, respectively, covering studies up to February 2023. The summary relative risks (RR) estimated by random-effects meta-analyses did not support an association between PFAS and overall breast cancer risk (e.g., a natural log (ln)-unit increase in serum/plasma concentrations [ng/mL] for perfluorooctanoate [PFOA] RR=0.95, 95% confidence interval [CI]:0.77-1.18; perfluorooctane sulfonate [PFOS] RR=0.98, 95%CI: 0.87-1.11). However, when limiting to studies that assessed exposures prior to a breast cancer diagnosis, we observed a positive association with PFOA (a ln-unit increase, RR=1.16, 95%CI: 0.96-1.40). We also observed some possible heterogeneous associations by tumor estrogen and progesterone receptor status among postmenopausal breast cancer cases. No meaningful changes were observed after excluding the studies with high risk-of-bias (Tier 3). Based on the evaluation tool developed by the National Toxicology Program, given the heterogeneity across studies and the variability in timing of exposure measurements, the epidemiologic evidence needed to determine the association between PFAS exposure and breast cancer remains inadequate. Our findings support the need for future studies with improved study designs to determine this association.

Residential proximity to toxic metal-emitting industrial sites and toenail metal concentrations in a United States-wide prospective cohort.

BACKGROUND: Industrial facilities across the United States (US) release millions of pounds of toxic chemicals, including metals. Exposure to toxic metals has been associated with adverse health outcomes, but there is limited evidence on the association between living near metal-releasing facilities and the body burden of emitted compounds. OBJECTIVE: To investigate the association between residential proximity to toxic metal-emitting industrial facilities and toenail metal concentrations and to evaluate whether associations differed by race. METHODS: In a sample of 1556 non-Hispanic Black (32.5%) and non-Hispanic White (67.5%) women from the Sister Study, we used the US Environmental Protection Agency Toxics Release Inventory to identify metal-emitting facilities within 3, 5, and 10 km of participants' baseline residences. We measured toenail concentrations (µg/g) of arsenic, cadmium, cobalt, chromium, and lead. Using multivariable linear regression, we examined associations between residential proximity to and emissions from metal-emitting facilities and toenail metal concentrations, stratifying by race. We explored modification of race-stratified associations by neighborhood deprivation, using the Area Deprivation Index (ADI). RESULTS: Black participants were more likely to reside within 3 km of chromium-releasing facilities and 5 and 10 km of all observed metal-emitting sites. Living near metal-releasing facilities was not associated with higher toenail metal concentrations overall. Among Black women, higher chromium emissions exposure was associated with higher toenail chromium levels (ßTertile3vs.non-exposed = 2.36 µg/g, 95% CI = 0.63, 4.10). An association with lead was observed among Black women residing in the most deprived areas (≥75th ADI percentile: ß = 3.08 µg/g, 95% CI = 1.46, 4.71). No associations were observed for White participants. CONCLUSIONS: Despite low exposure prevalence, our findings suggest that living near chromium- and lead-releasing facilities, especially at shorter distances, may be associated with higher corresponding toenail metal levels among Black women, particularly those residing in the most disadvantaged areas.

Understanding the role of environmental and socioeconomic factors in the geographic variation of breast cancer risk in the US-wide Sister Study.

OBJECTIVE: To describe the geographic pattern of breast cancer incidence in a nationwide prospective cohort and investigate whether environmental exposures and/or neighborhood socioeconomic status explain observed geographic disparities. METHODS: Using accelerated failure time models with a spatial random effect term, we mapped the health region-level association between residential location and breast cancer incidence for 44,707 participants in the Sister Study after controlling for established individual-level breast cancer risk factors. We performed a variable selection process to select environmental exposures [i.e., ambient nitrogen dioxide (NO2) and fine particulate matter (PM2.5), PM2.5 chemical composition, outdoor light at night (LAN), ambient noise, ultraviolet radiation, and greenspace] and neighborhood-level factors [i.e., population density and area deprivation index (ADI)] that predicted breast cancer incidence and quantified the spatial variation explained by the selected factors. We also considered whether the geographic pattern and predictors were similar when restricting to estrogen receptor-positive (ER+) tumors. RESULTS: We observed a spatial patterning in the incidence of overall breast cancer (Moran's I = 16.7, p < 0.05) and ER+ breast cancer (Moran's I = 13.2, p < 0.05), with a lower risk observed in the South and Southeast and a greater risk in the Northwest and certain areas of the Midwest and Northeast. NO2, LAN, and ADI explained 21.4% of the spatial variation in overall breast cancer incidence whereas NO2, PM2.5 chemical composition, LAN, greenspace, and ADI together explained 63.3% of the spatial variation in ER+ breast cancer incidence. CONCLUSIONS: Our findings provide additional evidence for a role of environmental exposures in breast cancer incidence and suggest that geographic-based risk factors may vary according to breast cancer subtype. Our findings support the need for additional research to quantify the relative contributions of geographic-based risk factors for breast cancer.

Outdoor air pollution exposure and uterine cancer incidence in the Sister Study.

BACKGROUND: Outdoor air pollution is a ubiquitous exposure that includes endocrine-disrupting and carcinogenic compounds that may contribute to the risk of hormone-sensitive outcomes such as uterine cancer. However, there is limited evidence about the relationship between outdoor air pollution and uterine cancer incidence. METHODS: We investigated the associations of residential exposure to particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2) with uterine cancer among 33 417 Sister Study participants with an intact uterus at baseline (2003-2009). Annual average air pollutant concentrations were estimated at participants' geocoded primary residential addresses using validated spatiotemporal models. Cox proportional hazards models were used to estimate hazard ratios and 95% confidence intervals for the association between time-varying 12-month PM2.5 (µg/m3) and NO2 (parts per billion; ppb) averages and uterine cancer incidence. RESULTS: Over a median follow-up period of 9.8 years, 319 incident uterine cancer cases were identified. A 5-ppb increase in NO2 was associated with a 23% higher incidence of uterine cancer (hazard ratio = 1.23, 95% confidence interval = 1.04 to 1.46), especially among participants living in urban areas (hazard ratio = 1.53, 95% confidence interval = 1.13 to 2.07), but  PM2.5 was not associated with increased uterine cancer incidence. CONCLUSION: In this large US cohort, NO2, a marker of vehicular traffic exposure, was associated with a higher incidence of uterine cancer. These findings expand the scope of health effects associated with air pollution, supporting the need for policy and other interventions designed to reduce air pollutant exposure.

Outdoor air pollution and histologic composition of normal breast tissue.

BACKGROUND: Biologic pathways underlying the association between outdoor air pollution and breast cancer risk are poorly understood. Breast tissue composition may reflect cumulative exposure to breast cancer risk factors and has been associated with breast cancer risk among patients with benign breast disease. Herein, we evaluated whether fine particulate matter (PM2.5) was associated with the histologic composition of normal breast tissue. METHODS: Machine-learning algorithms were applied to digitized hematoxylin and eosin-stained biopsies of normal breast tissue to quantify the epithelium, stroma, adipose and total tissue area from 3,977 individuals aged 18-75 years from a primarily Midwestern United States population who donated breast tissue samples to the Susan G. Komen Tissue Bank (2009-2019). Annual levels of PM2.5 were assigned to each woman's residential address based on year of tissue donation. We applied predictive k-means to assign participants to clusters with similar PM2.5 chemical composition and used linear regression to examine the cross-sectional associations between a 5-µg/m3 increase in PM2.5 and square root-transformed proportions of epithelium, stroma, adipose, and epithelium-to-stroma proportion [ESP], overall and by PM2.5 cluster. RESULTS: Higher residential PM2.5 was associated with lower proportion of breast stromal tissue [ß = -0.93, 95% confidence interval: (-1.52, -0.33)], but was not related to the proportion of epithelium [ß = -0.11 (-0.34, 0.11)]. Although PM2.5 was not associated with ESP overall [ß = 0.24 (-0.16, 0.64)], the association significantly differed by PM2.5 chemical composition (p-interaction = 0.04), with a positive association evident only among an urban, Midwestern cluster with higher concentrations of nitrate (NO3-) and ammonium (NH4+) [ß = 0.49 (0.03, 0.95)]. CONCLUSIONS: Our findings are consistent with a possible role of PM2.5 in breast cancer etiology and suggest that changes in breast tissue composition may be a potential pathway by which outdoor air pollution impacts breast cancer risk. This study further underscores the importance of considering heterogeneity in PM2.5 composition and its impact on breast carcinogenesis.

Opportunities, challenges, and future directions for simulation modeling the effects of structural racism on cancer mortality in the United States: a scoping review.

PURPOSE: Structural racism could contribute to racial and ethnic disparities in cancer mortality via its broad effects on housing, economic opportunities, and health care. However, there has been limited focus on incorporating structural racism into simulation models designed to identify practice and policy strategies to support health equity. We reviewed studies evaluating structural racism and cancer mortality disparities to highlight opportunities, challenges, and future directions to capture this broad concept in simulation modeling research. METHODS: We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Review Extension guidelines. Articles published between 2018 and 2023 were searched including terms related to race, ethnicity, cancer-specific and all-cause mortality, and structural racism. We included studies evaluating the effects of structural racism on racial and ethnic disparities in cancer mortality in the United States. RESULTS: A total of 8345 articles were identified, and 183 articles were included. Studies used different measures, data sources, and methods. For example, in 20 studies, racial residential segregation, one component of structural racism, was measured by indices of dissimilarity, concentration at the extremes, redlining, or isolation. Data sources included cancer registries, claims, or institutional data linked to area-level metrics from the US census or historical mortgage data. Segregation was associated with worse survival. Nine studies were location specific, and the segregation measures were developed for Black, Hispanic, and White residents. CONCLUSIONS: A range of measures and data sources are available to capture the effects of structural racism. We provide a set of recommendations for best practices for modelers to consider when incorporating the effects of structural racism into simulation models.