Answer Happy

describe the cohort, is rhe cohort population based oe exposure bassed? is the study prospective or retrospective?
indentify and discrible the exposure amd outcome ?
what stattistical test were used to measure the association between the exposure and outcome?
describe they key result
how can rhis evidence be used for healthcare decision making?
explain tour ideas future research?
Disclaimer: This is a machine generated PDF of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace original scanned PDF. Neither Cengage Learning nor its licensors make any representations or warranties with respect to the machine generated PDF. The PDF is automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. CENGAGE LEARNING AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION ANY WARRANTIES FOR AVAILABILITY. ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGEMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the machine generated PDF is subject to all use restrictions contained in The Cengage Learning Subscription and License Agreement and/or the Gale in Context: Opposing Viewpoints Terms and Conditions and by using the machine generated PDF functionality you agree to forgo any and all claims against Cengage Learning or its licensors for your use of the machine generated PDF functionality and any output derived therefrom Air Pollution, Clustering of Particulate Matter Components, and Breast Cancer in the Sister Study: A U.S.-Wide Cohort. Authors: Alexandra J. White, Joshua P. Keller, Shanshan Zhao, Rachel Carroll, Joel D. Kaufman and Dale P. Sandler Date: Oct. 1, 2019 From: Environmental Health Perspectives(Vol. 127, Issue 10) Publisher: National Institute of Environmental Health Sciences Document Type: Article Length: 7,078 words Content Level: (Level 5) Lexile Measure: 1520L DOI: http://dx.doi.org.my/library.wimu.edu/10.1289 HP5131 Full Text: Introduction Air pollution is classified by the International Agency for Research on Cancer (IARC) as a Group 1 carcinogen (Loomis et al. 2013), consistent with the epidemiologic evidence for the role of air pollution in lung cancer incidence (Hamra et al. 2015). However, less is known about the association between air pollution and breast cancer. Air pollution exposure is widespread and thus has the potential to have a substantial impact on the incidence of breast cancer, which is the most common cancer diagnosed among women in the United States (Siegel et al. 2019). Air pollution contains many carcinogens and other compounds that may act as endocrine disruptors-including polycyclic aromatic hydrocarbons (PAHs), metals, and benzene-which may influence breast cancer risk. Ecologic studies suggest that breast cancer risk is elevated in urban areas with higher air pollution in comparison with rural areas (Chen and Bina 2012: Wei et al. 2012). Some population studies have reported associations between air pollution and breast cancer, as reviewed by White et al. 2018, especially in studies that consider markers of traffic-related pollution such as nitrogen dioxide (NO sub 2), nitrogen oxides (NO.sub.x)), and PAH exposure (Bonner et al. 2005; Hystad et al. 2015: Mordukhovich et al. 2016; Ne et al. 2007: Reding et al. 2015). In the Sister Study cohort, Reding et al. (2015) reported a modest association between residential NO sub 2) levels and risk of estrogen and progesterone receptor-positive ([ER sup+] [PR sup.+) breast cancer. However, associations with measures of particulate matter (PM) <2.5 [micro]m and <10 (microjm in aerodynamic diameter ([PM sub 2.5] and [PM.sub.10), respectively) have not been consistently observed (Andersen et al. 2017a, 2017b; Hart et al. 2016: Reding et al. 2015; Villeneuve et al. 2018) Fine particulate matter (PM.sub.2.5]) is a complex mixture that varies in composition geographically due to varying sources. differences in meteorology, and other factors (Bell et al. 2007). Regional differences in particulate matter have been shown to modify the association with breast density, an important predictor of breast cancer risk (DuPre et al. 2017). Associations between [PM sub.2.5] and health effects such as blood pressure (Keller et al. 2017), cardiovascular disease (Brook et al. 2010), and mortality (Frankin et al. 2008) have been shown to vary significantly by (PM.sub.2.5) component profiles. In this report, we have extended our prior research on the relationship between air pollutants and breast cancer risk (Reding et al. 2015) with additional years of follow-up and case accrual and expanded this work to include consideration of effect measure modification by (PM.sub.2.5) components and breast cancer risk using predictive k-means clusters (Keller et al. 2017). We hypothesized that air pollution would be related to breast cancer risk and that associations for [PM.sub.2.5] would vary by [PM sub 2.5) component cluster. Breast cancer is a heterogenous disease (Polyak 2011). Associations with established breast cancer risk factors have been shown to vary by hormone receptor status (often defined by the presence or absence of the estrogen receptor (ER) and progesterone receptor (PR)| (Anderson et al. 2014) as well as by menopausal status at diagnosis (White et al. 2015). In addition, risk factors may vary by whether the tumor is invasive or ductal carcinoma in situ (DCIS) (Barclay et al. 1997). Previous research on the association between air pollution and breast cancer has been inconclusive on whether associations vary by these different outcome classifications; therefore, we also evaluated the risk associated with air pollutant exposure considering these different outcome definitions. Methods Study Population The Sister Study is a nationwide prospective cohort designed to investigate environmental and lifestyle risk factors for breast cancer (Sander et al. 2017). During 2003-2009, 50,884 women in the United States and Puerto Rico were recruited through a multimedia campaign, Women were eligible if they were between 35 and 74 y of age and had a sister who had been diagnosed with breast
style factors including information on their b cancer but had no history of breast cancer themselves & bene study participants completed an extensive computer assisted baseline telephone questionnaire that collected information on e udy participants demographics, medical and family history, and reproductive and characteristics. Al participants provided signed informed consent, and the Sister Study was approved by the national review boards of the National Institute of Environmental Sciences, National Institutes of Health and the Copemous Grip This study relied on Sister Study Data Release 6.0, which included follow-up data through 15 September 2016. For this analysis only women living in the contiguous United States re ligble in-49.771) ( Outcome Classication Sister Study participants are contacted annually for heath updates, including for information on any incident breast cancer diagnoses. Participants additionally complete detailed to up questionnaires every 2-3y to update testyle and risk factor information and to report any other health updates. Response rates have remained over 90% , 91-90% throughout follow-up. We obtained medical records and pathology reports, from which tunor receptor information was obtained Curenty, over 80% of breast cancer diagnoses have been confirmed through medical records Agreement between medical records and self-report of breast cancer and tumor characteristics is very high over 99% for breast cancer overall Invasive breast cancer and DCS coroined was th the outcome by invasive versus DCS, contined ERPR breast cancer diagnosis prior to completion of all seine prior however, we explored eterogenety in at diagnosis. We excluded women with a known time of diagnosis (62) As previously described (Reding et al. 2015), polufion measures P sue 25 PM sub 10 and NO sub 2) were estimated for Sister Study participants based on the annual average concentrations at their addresses during the 12 months prior to evolment, an derived using monitoring data from 2006 (for PM sub 25 and NO sub 20 and 2000 (for PM sub 100. Annual averages of air pollution concentration were estimated at each participants hone using a waldated regionalized universal leiging model with spatial anothing, which incorporated information from regulatory monitors and a large number of geographic covariates, including some derived from satelite observations, as previously described Sampson at 2013 Young of 2016) NEOsub 2) estimates could not be obtained for e-69 participants whose addresses could not be geocoded or for locations in which there was incomplete satelite coverage For the PM Sun 25 component analysis, data were obtained from 130 US EPA Ar Quality System monitoring location in 2010 that measured mass concentrations EC), organic carbon (OC), (NOsup substate (50 sup2a S. Se, NV and 2 Mass each species by the annual average PM2.5 concentrations were converted to mass tractions by dividn at that location. The mass tractions w Stical Analys We first evaluated the association between an equal range increase in air pollutants in relation to incident breast cancer using Cox proportional hazards model to estimate hazards and confidence intervals (Cis. The time scale for the Cax model was age, and the women were folowed hom age at aty entry un age at breast cancer diagnosis or age at the end of follow up, with consoring for death or loss to follow-up deviations from the proportional hazards assumption by using hood ratio est to compare models with and without interactions for air pollutants and time We considered whether associations varied for invasive breast cancer versus DCS, whether the cancer was diagnosed pre-versus postmenopause, and by tumor subtype defined using combined ER and PR statusin models evaluating the association for premenopausal breast cancer, we censored women at Cox model at the age at which they enved in the study analyses, women were censored if they were diagnosed and PR positive ER su PR sup] breast cancer, wo censored at their age of diagnosis menopause For postmenopausal breast cancer, women entered the chever was later. For more another spe. For example, when the outcome of interest was ER wo were diagnosed with ER or PR-negative breast cancer were To assess the impact of PM sub 25 composition on breast cancer risk, we evaluated associations between 2010 (PM sub 251 and incident breast cancer strated by PMsub 25) components using previously developed predictive k-means clusters (Keller at al 2017) [PM sub 25 component information was not avale year used in our primary analysis described above, so this analysis used exposure estimates from 2010 the year for which component data were available. Clustering is a method of dimension reduction that can be used to parton mus-poutant deservations into a prespected number (k) of clusters The covariate adaptive approach used here clustered monitor locations using the multidimensional component mass fractions whe also allowing the geographic covariates at each location to influence chuster membership resulting in groups of monitor locations with similar component profiles Cluster membership was the predicted for each shty participant based on the geographic covariatest their residential location Participants were assigned to the user to which they had the highest probability of belonging This method has been shown predictive and power for effect modification than using traditional means clustering, which does not incposle geographic vartates in cluster identification. The number of clusters and the covariates were selected by 10-od crosswalidation. The final selected model had eight clusters, as detailed previously eller et al. 2017). Cluster 8 to which 74 participants belonged) was not included in the analyses due to it smal sample size. For this study, we estimated the association betwen 2010 PM sub 25 and breast cancer rak strated by cur (Figure 1) We tested for effect modification using a keihood to test to compare models with and without interaction ten between PM sub 2.5 and indicator variables for the clusters The covariate adjustment set included age, racelethnicity on Hispanic woher education high school degrevalent or less, some college, 4-y degree or higher smoking status inever mer current and menopausal hormone therapy (ever, never) be consistent with our prior publication Reding et al 2015 As a secondary analysis, we included additional confounders including household income.census-tract income mastupa A We evaluated effect measure modification by years spent ving at the home icy greater than or equy census-defined geographic region (Northeast, Midwest South West based on state of residence degree of family history of breast cancer (1 fir-degree family member, at find-degree family member), M (25 kgm sup225 tog han or equal to 30 gm sup20 and comp hormone use (ever, never) by including a cross-product in the Cox model and using a kalhood ratio test Given the correlation between region and PM sub 25 component clusters in anested by region we also considered adjustment for [PM sub 2.5 cluster and in analyses stratified by PMsub.25 duster we so considered adjustment for region. To evaluate whether differences by region were explained by other factors, we considered the inclusion of multiple additional action terms within a single model (between air pollutant and region, air pollutant and cluster of missing data therefore, we conducted a complet covariales) with a resulting sample size of 47,433 Manda polutant and education Covaats had cluding those with missing values for the adjustment All analyses were conducted using SAS (version 4 SAS Routs During an average of 8.4 y of lolow-up, there were 2.852 incident breast cancer cases (2.225 invasive and 623 DCIS) Study participant baseline characteristics have been onviously oublished Sander et al 2017 Biety, the median soe atenent was
Resu During an average of 8.4y of follow-up, there were 2.852 incident breast cancer cases (2.225 invasive and 623 DCIS) Study participant baseine characteristics have been previously published (Sander et al. 2017). Briety, the median age at enrolment was 55.6 y. Women in the study are predominately non-Hispanic white (83.7%), reported being married or living as maed (74.7%, and over hat have a bachelor's degree or higher. The Sister Study includes participants from each of the contiguous states, with representation ranging from 0.2% participants from Wyoming to 8.5% from Caltomia. Participant characteristics by geographic region are displayed in Table 1. ( An OR in NO.sub 21 (58 ppt was associated with breast cancer risk overal -1.08 (95% 1.03 1.13 (2) W observed substantial heterogeneity when stratifying by invasive disease versus DCIS and therefore show these results separately This association was stronger for DCS 1.23 (95% CE 1.12, 1.35 than for invasive breast cancer H 1.02 (95% CI: 0.96 1.07 Similarly, PM sub 25 (OR=36 micolon sup 30 was positively associated with DCIS incidence HR 1.16 (95% Cr 1.02 1.31 but not invasive breast cancer (HR 103 (95% CI 0.96 1.00 No elevated HRs were observed in relation to PM sub 10 (IOR-5.8mcom.sup.3). Further adjustment for other known and established breast cancer risk factors and other markers of socioeconomic status, including household income, cenous-tract income, marital status, panty, and GM, did not matotally change The point estimates An IQR increase in NO.sub 2) was inversely associated with ER sup-|PR sup-breast cancer HR-0.87 (36% 0.73, 1.04 not with ER sup+] PR sup breast cancer (HR=1.03 (95% C095, 1.10 (Table 3) Associations for PM sub 25 and (PM sub 10 did not vary by ER PR status of the tumor. We did not observe notable heterogeneity in the observed associations by menopausal status at diagnose Table 51 Associations for invasive breast cancer and exposure to PM sub.25) sub heterogeneity004) (PM10) (subheterogeneity)-004), and NO sub 2) sub heterogeneity)-0.05) all varied notably by geographic region (Table 4). An OR si PM sub 251 HR-1.14 (95% CE 1.02 127 was associated with invasive breast cancer in women residing in the West but not other geographic regions Northeast HR-0.80 (95% CI 0.73.107) Midwest HR-033 (95% CIDB1, 1.081 South HR -1.00 (95% CE 0.00, 1.17 A similar trend with a slightly higher HR among women in the Western United States was observed for (PM10 exposure. An IQR increase in NO sub 2) was similarly associated with breast cancer among women living in the West HR=1.00 (95% CI 0.99, 1.21 as well as for women residing in the South pit 1.16 95% C 101, 1.331 For DCIS in generale observed associations to be more pronounced in women living in the Northeast or the Midwest. For example for an IOR increase in PM sub 25, we observed an HR 1.35 (95% CR 0.97, 1.88) for women in the Northeast and HR 1.68 (95% CI 1.21.2.34) for women in the Midwest The patten was similar for [PM sub.10] (p sub heterogeneity)-001) For NO sub 25 risk of DCIS also vanied by region pub heterogeneity)-001), with the highest HRs observed in the Midwet HR 1.73 (16% CI:1.30,214 These associations persisted with further covariate adjustment and when including IPM sub 2.5) component clusters in the model. These associations were also robust to the inclusion of additional interaction terms with cluster, BM, and education in the models Table 52) Overall, the associations for (PMsub.2.5) using 2010 air pollution estimates (2010 10-2.9 microp sup 3) were similar to those from our main results using data from 2006 jeg. 2010 invasive HR 1.01 (95% CI 0.95, 107) vs 2006 sive H 1.00 (95% C 0.96 1.09 (Table 5). Consistent with the results stratified by geographic region, invasive breast cancer risk also varied by PM sub 25 component cluster p sub heterogeneity 0.30 (Table 5) Specifically, we observed an elevated rek of invasive breast cancer associated with PM sub 25 exposure for both Cluster 4 (Caltomia: Figure 1) and Cluster 7 (West Figure 1) but no increase in risk for women in any of the other clusters. The Califomia monitors were captured in Cluster 4 (Figure 1), which was characterized by having low S tractions and large fractions of Na and NEOsup sub3 (Figure 23, indicating exposure to maine aerosols and agricultural emissions (Keter et al. 2017). For an IQR increase in PM sub 2.5 for women who were assigned to Cluster 4 w observed a 25% higher risk of invasive breast cancer et 25 (95% C 0.97, 160 Cluster 7 was aha centered in the Wester United States Figure 1), and was defined by high tractions of SL Ca K, and Al (Figure 2), consistent with the surfaces in this geographic region (Shackdette and Boemgen 1964). For women in Cluster 7, we also observed an elevated risk associated with an OR increase in PM sub 25) HR 1.60 (95% CI: 090, 285, but the estimate for this cluster was imprecise due to the smal number of cases in-59). These associations remained similar with further adjustment for additional covariates and inclusion of geographic region in the adjustment set. For DCIS, though sample sizes were small, there was less evidence of risk heterogeneity by cluster (asub heterogeneity-0.9) (Table 5) Across the clusters, PM sub 2.5) was positively associated with DOIS in alt but Custer 7. For example, a higher risk of DCIS in relation to an IQR increase in PM sub 2.5) was observed for women in Cluster 1-1.38 (95% CI 1.02.10 and Cluster 2 1.37 (95% CE 1.00, 1.830 Cluster 1 is in the Midwest and Mid-Atlantic region (Figure 1) with above-average NO sup-sub 3 and 50 sup 2-sub 4) (Figure 2), which is consistent with high ambient ammonia levels from agriculture Cluster 2 in the Northeast (Figure 1) and is characterized by higher tractions of Cd. V, and Ni (Figure 2) Elevated HRs, but with wide Cls, ware also observed for women in Cluster 3 HR-1.22 (95% CI 0.75, 1988, Custer 4 HR-1.33 (95% CI: 0.80.2.220 Cluster 5 HR- 1.18 (95% CI: 0.67,207, and Cluster 6 HR-122 (95% CE 0:35.4.26 nation to DCIS We observed no significant effect measure modification of the associations between any of the air pollutants and breast cancer risk by time spent living at the baseline residence joee Table 53). However, we did note an elevated HR for invasive breast cancer was observed for PM sub 251 in women who lived in their residences for greater than or equal to 10y = 1.07 (95% C098 1.17) We observed modification by obesity, women who had a BMx30 kg sup 2 had a higher risk of invasive breast cancer associated with PM sub.2.5 HR-119 (95% C11.08 1.341.jp sub heterogeneity 0.02) and NEO sub 21 HR-1.11 (95% CI 1.01, 121). sub heterogenen-0.1] (see Table 54). We observed no significant effect measure modification of the associations for air pollutants and breast cancer rk by extent of breast cancer family history or hormone therapy use (see Tables 55 and 56) As expected, there was substantial overlap between clusters and geographic region (Table 57) Discussion in this large. US wide prospective cohort study, we evaluated the association between air pollutants and breast cancer risk and demonstrated that air pollution levels were related to both invasive breast cancer and DCIS in certain geographic regions For example, exposure to PM sub 25) tended to be related to invasive breast cancer risk in the Western United States, whereas for DOIS, the associations were most evident among women in the Northeast and Midwest. These results were consistent with our analysuizing predictive means clustering to evaluate PM sub 2.5) component mixtures in relation to breast cancer (PM sub 25 levels in Western-based clusters were related to the risk of invasive breast cancer, whereas PM sub 25 exposure in other clusters were more strongly related to the risk of DCIS. Together, these results suggest that consideration of geographic variability in air pollution is crucial when evaluating associations with breast cancer. This is the first US-based study to evaluate the relationship between PM components and breast cancer risk Air pollution is plausibly related to breast cancer given that it is a complex mature containing numerous carcinogens and endoorne disruptors (Loomis et al 2013). In breast cancer cell lines, PM has been shown to have estrogenic properties and oxidative stre related DNA-damaging activity (Chen et al. 2013) inhaled toxicants can reach the breast foue Hill and Wynder 1979 and trac related air pollution has been associated with aberant DNA methylation in breast cancer related genes measured in tumore (Whise et al 2016). Air pollution has also been related to higher breast density (DuPre et al. 2017: White et al 2010: Yaghyan et al 2017), a marker of breast cancer risk
2017), a marker of breast cancer risk Markers of polution such as NO sub 23 NO subx) and PAH exposure have been found to be associated with breast cancer risk (Bonner et al 2005: Hystad et al. 2015; Mordukhovich et al 2016: Ne et al. 2007, Reding et al. 2015), wherefor measures of PM have been mostly null (Andersen et al. 2017 20170: Hart et al. 2016: Roding et al. 2015: Vieneuve et al 2018 However, these studies have largely not considered the impact of geographic variability or PM heterogeneity. For example, although we too saw ite consistent evidence of an association with PM sub 2.5 or PM sub 10 and invasive breast cancer in our nationwide study population, stratifying by region elucidated significant variability in the associations Air pollution is a complex mixture and it is important to address the heterogeneity of this exposure and to evaluate how that may impact breast cancer risk Only one prior study has evaluated PM components with breast cancer. In a pooled analysis of European cohorts Andersen et al (2017) considered PM components individualy in relation to postmenopausal breast cancer risk. They observed a higher breast cancer risk for exposure to both PM sub 2.5) and PM sub 101 V and PM sub 10 Ni levels Importantly considering a single PM component at a time does not address the correlated nature of the PM components. To better capture this heterogeneity, we utilized predictive means clustering, which is a data raduction technique that identities subgroups of individuals who are exposed to similar combinations of PM components. This permits the identification of PM componentures and consideration of how these complex matures influence the association between PM sub 25 and breast cancer We observed heterogeneity by geographic region and PM sub 25) component cluster, individually and after simultaneous adjustment in the associations between air pollutants and breast cancer risk. Although this geographic variability has not been explicitly considered previously in relation to breast cancer, DuPre et al (2017) observed geographic variation in that PM2.5 in the Nurses Health Study was related to breast density only among participants iving in the Northeast in our study. [PM sub 2.5 was related to DCIS across most of the clusters despite lower power to detect associations. In contrast, PM sub 2.5) was associated with invasive breast cancer only in women assigned to two Westem based clusters (Clusters 4 and 7), consistent with our regional results finding a higher risk among women living in the Western United States Cluster 4 which encompassed the California monitors was characterized by having low fractions of S and large fractions of Na and NO sup-sub 33, indicative of marine aerosols and agricultural emissions, Airbome exposure to pesticides from agricultural practices may contribute to cancer risk (Engeletal 2005 Lee et al. 2002: Leno et al. 2015). Cluster 7, which was more widely spread across the Westem United States, had high fractions of 5. Ca, K, and Al, consistent with the surface sot in this region (Shacklete and Bloemgen 1064). In a subset of our study population with DNA methylation data, among women in Clusters 4 and 7, PM sub.2.5) was also associated with ONA methylation-based biologicage acceleration (White et al. 2019a, a marker of uture breast cancer risk Kresovich et al. 2019). These consistent findings support a role for these clusters of PM sub 25) components in breast carcinogenesis Differences between overall results for invasive breast cancer and DCIS wers unexpected. DCIS is generally thought to be a precursor to invasive breast cancer, and risk factor profiles for DCIS and invasive disease are similar although there are some differences (Reeves et al 2012). However, it is possible that variation in socioeconomic status by region may have contributed to differences in access to health care that could have influenced the associations observed with OCIS, which is primarily detected by screening (Vig et al 2010) To address this, we further adjusted our models for risk of DCIS for individual and census tract eve socioeconomic variables, but we did not observe a change in results. It is unlikely that screening practices explain these results because over 92% of women in our study population were screened within the last 2 y. This high rate of screening may not be too surprising given that our study population consists of women with a family history of breast cancer among whom regular screening is very common in addition, mammographic screening did not vary by geographic region, so geographic differences in screening behaviors or access cannot explain observed differences in associations by region or cluster. Despite extensive efforts to address potential residual confounding it remains possible that there is some unaddressed confounding from other factors such as noise or other pollutants that may be diving the differences in DCISInvasive disease risk by region. Another potential explanation is that these mixtures of pollutants simply contributo differently to breast cancer risk by stage of disease, perhaps by incingumo growth rate Our results of a higher risk of DCIS in relation to air pollutants in the Northeast are consistent with results from a study of women on Long Island, New York, for whom higher vehicular traffic air polution was similarly associated with DCIS (Mordovich al 2016) We did not observe substantial evidence of vanability in the associations of overat air pollutant exposures and breast cancer by menopausal status or by tumor subtype. However, a imitation of this study was that, despite our large sample size, we were unable to explore effect measure modification by cluster with consideration of tumor subtype We observed that invasive breast cancer risk associated with exposure to PM sub 25 and NEO sub 21 was higher among women with a greater than or equal to 30 kgm sup.23 suggesting a possible synergistic relationship between obesity and air pollution Components of air pollution, such as PAs, are pophilic (IARC 2010), whereas other components, such as metals, have been detected in visceral fat (Gin et al. 2010). Thus, fat tissue may serve as a possible reservoir for which the constituents of air pollution may accumulate. This finding is consistent with prior research on PAHS (Nehoff et al 2017) and airbome metals (White et al 2016) A strength of this study was the use of predictive means clustering to determine subgroups of women who were exposed to different PM sub 2.5 component midures. Consideration of the mature is important because PM is not a homogenous exposure and our approach permitted a more refined and nuanced exposure assessment. The predictive means approach used to identity and assign PM component clusters in the Sister Study was an unsupervised method, meaning that the clusters are useful for a public heath-focused approach to identity existing air pollution modures and determine how they are related to heat outcomes However, given that breast cancer case status was not included in the identification of these clusters, it is possible that there are some groups of pollutants that may be more strongly related to breast cancer risk that were not identified. Although these clusters incorporate 22 different PM sub 25) components, it is possible that these clusters may be influenced by other comelated unmeasured air pollutants. In addition, the accuracy of the concentration measurements may vary for some of the Mu components and thus may result in differential measurement error. Furthermore, we classifed individuals into the chuster for which each person had the highest probability of membership, and there is uncertainty in the cluster predictions that could also lead to exposure measurement error. Finally, we cannot rule out the possibility of residual spatial confounding The Sister Study is a prospective cohort with extensive covariate information. A strength of this study is the use of land-use regression models with spatial smoothing to assess exposure to air pollution at the level of cohort evolment residence. However, a imitation of this approach is that we used air pollution measures estimated around the time of enrolment in the study on average y prior to breast cancer diagnosis). This measurement may not represent the most relevant time period of exposure with respect to breast cancer etiology. We did, however, consider duration of residence at the current residence. It is noteworthy that most results did not differ for women with 10 or greater than or equal to 10 y all their enrollment address is possible that more long-term exposure, or exposure occurring during hypothesized susceptible windows of exposure including childhood Bonner et al 2005; et al. 2007; Shmuel et al. 2017), or exposure during the reproductive time period may be more relevant In conclusion in this lange prospective US-wide cohort, we observed that measures of air pollution, including sub PM sub 2.5), and PM sub 10 were related to both invasive and DCIS breast cancer when straying by geographic region Using predictive means clusters to consider the potential modifying role of (PM sub 25] components, we observed that the risk of breast cancer varied based on PM sub 25) component clusters, which were who comelated with geographic region. This study supports a relationship between air pollution and both invasive breast cancer and DCIS risk within certain geographic subgroups and emphasizes the need to consider variability in air pollution measures by geographic region and composition of the mature, as well as by tumor staging, when assessing associated risks with breast cancer
Abstract: Background: Particulate matter (PM) is a complex mixture. Geographic variations in PM may explain the lack of consistent associations with breast Objective: We aimed to evaluate the relationship between air pollution, PM components, and breast cancer risk in a United States- wide prospective cohort. Methods: We estimated annual average ambient residential levels of particulate matter <2.5 [micro]m and <10 [micro]m in aerodynamic diameter ([PM.sub.2.5] and [PM.sub.10], respectively) and nitrogen dioxide (N[O.sub.2]) using land-use regression for 47,433 Sister Study participants (breast cancer-free women with a sister with breast cancer) living in the contiguous United States. Cox proportional hazards regression was used to estimate hazard ratios (HRS) and 95% confidence intervals (Cls) for risk associated with an interquartile range (IQR) increase in pollutants. Predictive k-means were used to assign participants to clusters derived from [PM.sub.2.5] component profiles to evaluate the impact of heterogeneity in the [PM.sub.2.5] mixture. For [PM.sub.2.5], we investigated effect measure modification by component cluster membership and by geographic region without regard to air pollution mixture. Results: During follow-up (mean = 8.4y), 2,225 invasive and 623 ductal carcinoma in situ (DCIS) cases were identified. [PM.sub.2.5) and N[O.sub.2] were associated with breast cancer overall (HR= 1.05 (95% CI:0.99, 1.11) and 1.06 (95% CI:1.02, 1.11), respectively] and with DCIS but not with invasive cancer. Invasive breast cancer was associated with [PM.sub.2.5] only in the Western United States [HR=1.14 (95% CI:1.02, 1.27)] and N[O.sub.2] only in the Southern United States [HR= 1.16 (95% CI:1.01, 1.33)]. [PM.sub.2.5] was associated with a higher risk of invasive breast cancer among two of seven identified compositionbased clusters. A higher risk was observed [HR = 1.25 (95% CI: 0.97, 1.60 ) ] in a California-based cluster characterized by low S and high Na and nitrate (N[O.sup.-.sub.3]) fractions and for another Western United States cluster [HR = 1.60 (95% CI: 0.90,2.85)], characterized by high fractions of Si, Ca, K, and Al. Conclusion: Air pollution measures were related to both invasive breast cancer and DCIS within certain geographic regions and PM component clusters. https://doi-org.mylibrary.wilmu.edu/10.1289/EHP5131 White, Alexandra J. "Keller, Joshua P.^Zhao, Shanshan^Carroll, Rachel Kaufman, Joel D.^Sandler, Dale P. Copyright: COPYRIGHT 2019 National Institute of Environmental Health Sciences http://www.ehponline.org/ Source Citation (MLA 8th Edition) White, Alexandra J., et al. "Air Pollution, Clustering of Particulate Matter Components, and Breast Cancer in the Sister Study: A U.S.- Wide Cohort." Environmental Health Perspectives, vol. 127, no. 10, 2019, p. 107002. Gale In Context: Opposing Viewpoints, https://link-gale-com.mylibrary.wilmu.e ... d=7df7a7b8. Accessed 5 Jan. 202 Gale Document Number: GALEIA604895379