ResearchOpen Access

Air Pollution, Clustering of Particulate Matter Components, and Breast Cancer in the Sister Study: A U.S.-Wide Cohort

    Published:CID: 107002https://doi.org/10.1289/EHP5131Cited by:39

    Abstract

    Background:

    Particulate matter (PM) is a complex mixture. Geographic variations in PM may explain the lack of consistent associations with breast cancer.

    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 <2.5μm and <10μm in aerodynamic diameter (PM2.5 and PM10, respectively) and nitrogen dioxide (NO2) 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 (CIs) for risk associated with an interquartile range (IQR) increase in pollutants. Predictive k-means were used to assign participants to clusters derived from PM2.5 component profiles to evaluate the impact of heterogeneity in the PM2.5 mixture. For PM2.5, 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. PM2.5 and NO2 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 PM2.5 only in the Western United States [HR=1.14 (95% CI:1.02, 1.27)] and NO2 only in the Southern United States [HR=1.16 (95% CI:1.01, 1.33)]. PM2.5 was associated with a higher risk of invasive breast cancer among two of seven identified composition-based 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 (NO3) 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/10.1289/EHP5131

    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 (NO2), nitrogen oxides (NOx), and PAH exposure (Bonner et al. 2005; Hystad et al. 2015; Mordukhovich et al. 2016; Nie et al. 2007; Reding et al. 2015). In the Sister Study cohort, Reding et al. (2015) reported a modest association between residential NO2 levels and risk of estrogen and progesterone receptor-positive (ER+PR+) breast cancer. However, associations with measures of particulate matter (PM) <2.5μm and <10μm in aerodynamic diameter (PM2.5 and PM10, 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 (PM2.5) 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 PM2.5 and health effects such as blood pressure (Keller et al. 2017), cardiovascular disease (Brook et al. 2010), and mortality (Franklin et al. 2008) have been shown to vary significantly by PM2.5 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 PM2.5 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 PM2.5 would vary by PM2.5 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 (Sandler 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 cancer but had no history of breast cancer themselves. At baseline, study participants completed an extensive computer-assisted baseline telephone questionnaire that collected information on each study participant’s demographics, medical and family history, and reproductive and lifestyle factors including information on their baseline residential characteristics. All participants provided signed informed consent, and the Sister Study was approved by the institutional review boards of the National Institute of Environmental Sciences, National Institutes of Health, and the Copernicus Group. 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 were eligible (n=49,771).

    Outcome Classification

    Sister Study participants are contacted annually for health updates, including for information on any incident breast cancer diagnoses. Participants additionally complete detailed follow-up questionnaires every 2–3 y to update lifestyle and risk factor information and to report any other health updates. Response rates have remained over 90% (i.e., 91–96%) throughout follow-up. We obtained medical records and pathology reports, from which tumor receptor information was obtained. Currently, 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 (D’Aloisio et al. 2017), with a positive predictive value over 99% for breast cancer overall. Invasive breast cancer and DCIS combined was the main outcome of interest a priori; however, we explored heterogeneity in the outcome by invasive versus DCIS, combined ER/PR status, and menopausal status at diagnosis. We excluded women with a breast cancer diagnosis prior to completion of all baseline data collection or an unknown time of diagnosis (n=62).

    Exposure Classification

    As previously described (Reding et al. 2015), air pollution measures (PM2.5, PM10, and NO2) were estimated for Sister Study participants based on the annual average concentrations at their addresses during the 12 months prior to enrollment, as derived using monitoring data from 2006 (for PM2.5 and NO2) and 2000 (for PM10). Annual averages of air pollution concentration were estimated at each participant’s home using a validated regionalized universal kriging model with spatial smoothing, which incorporated information from regulatory monitors and a large number of geographic covariates, including some derived from satellite observations, as previously described (Sampson et al. 2013; Young et al. 2016). NO2 estimates could not be obtained for n=69 participants whose addresses could not be geocoded or for locations in which there was incomplete satellite coverage.

    For the PM2.5 component analysis, data were obtained from 130 U.S. EPA Air Quality System monitoring locations in 2010 that measured mass concentrations for 22 PM2.5 component species [elemental carbon (EC), organic carbon (OC), nitrate (NO3), sulfate (SO42), Al, As, Br, Cd, Ca, Co, Cr, Cu, Fe, K, Mn, Na, S, Si, Se, Ni, V, and Zn]. Mass concentrations were converted to mass fractions by dividing the annual average of each species by the annual average PM2.5 at that location. The mass fractions were log transformed.

    Statistical Analysis

    We first evaluated the association between an interquartile range (IQR) increase in air pollutants in relation to incident breast cancer using Cox proportional hazards model to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). The time scale for the Cox model was age, and the women were followed from age at study entry until age at breast cancer diagnosis or age at the end of follow-up, with censoring for death or loss to follow-up. We tested for deviations from the proportional hazards assumption by using likelihood ratio tests to compare models with and without interaction terms for air pollutants and time.

    We considered whether associations varied for invasive breast cancer versus DCIS, whether the cancer was diagnosed pre- versus postmenopause, and by tumor subtype (defined using combined ER and PR status). In models evaluating the association for premenopausal breast cancer, we censored women at age at menopause. For postmenopausal breast cancer, women entered the Cox model at the age at which they enrolled in the study or at their age of menopause, whichever was later. For tumor subtype analyses, women were censored if they were diagnosed with another subtype. For example, when the outcome of interest was ER- and PR-positive (ER+PR+) breast cancer, women who were diagnosed with ER- or PR-negative breast cancer were censored at their age of diagnosis.

    To assess the impact of PM2.5 composition on breast cancer risk, we evaluated associations between 2010 PM2.5 and incident breast cancer stratified by PM2.5 components using previously developed predictive k-means clusters (Keller et al. 2017). PM2.5 component information was not available for this study for 2006, the 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 partition multi-pollutant observations into a prespecified number (k) of clusters. The covariate-adaptive approach used here clustered monitor locations using the multidimensional component mass fractions while also allowing the geographic covariates at each location to influence cluster membership, resulting in groups of monitor locations with similar component profiles. Cluster membership was then predicted for each study participant based on the geographic covariates at their residential location. Participants were assigned to the cluster to which they had the highest probability of belonging. This covariate-adaptive clustering method has been shown to provide better predictive accuracy and power for detecting effect modification than using traditional k-means clustering, which does not incorporate geographic covariates in cluster identification. The number of clusters and the covariates were selected by 10-fold cross-validation. The final selected model had eight clusters, as detailed previously (Keller et al. 2017). Cluster 8 (to which n=74 participants belonged) was not included in the analyses due to its small sample size. For this study, we estimated the association between 2010 PM2.5 and breast cancer risk stratified by cluster (Figure 1). We tested for effect modification using a likelihood ratio test to compare models with and without interaction terms between PM2.5 and indicator variables for the clusters.

    Figure 1 depicts the maps of Midwest, Northeast, West, and South with clusters 1 to 7.

    Figure 1. Predicted PM2.5 component cluster membership by geographic region (jittered to protect confidentiality), Sister Study, 2003–2009. Figure adapted from Keller et al. (2017). PM2.5, particulate matter <2.5μm in aerodynamic diameter.

    The covariate adjustment set included age, race/ethnicity (non-Hispanic white, other), education (high school degree/equivalent or less, some college, 4-y degree or higher), smoking status (never, former, current), and menopausal hormone therapy (ever, never) to be consistent with our prior publication (Reding et al. 2015). As a secondary analysis, we included additional confounders including household income, census-tract income, marital status, parity, and body mass index (BMI). We evaluated effect measure modification by years spent living at the home (<10y, 10y), census-defined geographic region (Northeast, Midwest, South, West based on state of residence), degree of family history of breast cancer (1 first-degree family member, >1 first-degree family member), BMI (<25kg/m2, 25to<30kg/m2, 30kg/m2), and postmenopausal hormone use (ever, never) by including a cross-product term in the Cox model and using a likelihood ratio test. Given the correlation between region and PM2.5 component clusters, in analyses stratified by region we also considered adjustment for PM2.5 cluster and in analyses stratified by PM2.5 cluster we also considered adjustment for region. To evaluate whether differences by region were explained by other factors, we considered the inclusion of multiple additional interaction terms within a single model (between air pollutant and region, air pollutant and cluster, air pollutant and BMI, and air pollutant and education). Covariates had <4% missing data; therefore, we conducted a complete case analysis (excluding those with missing values for the adjustment covariates), with a resulting sample size of n=47,433.

    All analyses were conducted using SAS (version 9.4; SAS Institute Inc.).

    Results

    During an average of 8.4 y of follow-up, there were 2,852 incident breast cancer cases (2,225 invasive and 623 DCIS). Study participant baseline characteristics have been previously published (Sandler et al. 2017). Briefly, the median age at enrollment was 55.6 y. Women in the study are predominately non-Hispanic white (83.7%), reported being married or living as married (74.7%), and over half 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 California. Participant characteristics by geographic region are displayed in Table 1.

    Table 1 Study population characteristics by geographic region, Sister Study, 2003–2009.

    Table 1 has five columns. The first column lists population characteristics. The adjacent columns list N and percentage values for the following geographic regions: Midwest (N equals 13,047), Northeast (N equals 8,082), South (N equals 15,960), and West (N equals 10,344).
    CharacteristicGeographic Region
    Midwest (n=13,047)Northeast (n=8,082)South (n=15,960)West (n=10,344)
    n(%)n(%)n(%)n(%)
    Age at baseline (y)
    451,845141,139142,123131,27912
     46–492,097161,344172,429151,51715
     50–542,581201,594203,179201,96019
     55–592,620201,535193,147202,09420
     60–641,877141,167142,454151,58615
    652,027161,303162,628161,90818
    Race
     Non-Hispanic white12,000897,3659112,000779,00687
     Other1,3801171793,669231,33813
    Education
    4-y college degree6,132474,469558,140515,49153
    High school degree or equivalent2,337181,197152,354151,24712
     Some college/technical school4,578352,416305,466343,60635
    Household income
    $50,000<$100,0005,781443,280416,369404,11840
    <$50,0003,403261,741224,130262,46724
    >$100,0003,863303,061385,461343,75936
    Census tract–level income
    $50,000<$100,0008,137625,115638,317526,29561
    <$50,0004,096311,983256,444403,10530
    >$100,0008146984121,19989449
    Smoking status
     Never smoker1,202962981,44596787
     Former smoker7,473574,045508,965565,98358
     Current smoker4,372343,408425,550353,68336
    Marital status
     Married or living as married10,000776,0227512,000737,78375
     Never married6505543788864985
     Widowed, divorced, or separated2,388181,517193,480222,06320
    BMI (kg/m2)
    <24.94,207322,494315,067323,21031
    2529.94,609353,342415,797364,37942
    304,231322,246285,096322,75527
    Ever HRT
     No7,332565,294668,327525,10149
     Yes5,715442,788347,633485,24351
    Parity
     None2,032161,570192,920182,17621
     11,659131,124142,662171,55315
     2–37,754594,661588,997565,59454
    >31,6021272791,38191,02110
    Mammographic screening in last 24 months
     No936849361,24787648
     Yes11,000927,1599414,000928,95192
     Missing7464301,116629
    Family history of breast cancer
     1 first-degree relative9,476736,0317512,000737,53973
    >1 first-degree relative3,571272,051254,239272,80527
    Baseline menopausal status
     Postmenopausal8,526655,1276311,000687,00768
     Premenopausal4,468342,927365,038323,29532
     Missing53284642
    Breast cancer characteristics
     Invasive6135348475855065
     DCIS1651113120311421
    ER+ invasive4423264355333854
    ER invasive931421901531

    Note: —, not applicable; BMI, body mass index; DCIS, ductal carcinoma in situ; ER, estrogen receptor; HRT, hormone replacement therapy.

    An IQR increase in NO2 (5.8ppb) was associated with breast cancer risk overall [HR=1.08 (95% CI: 1.03, 1.13)] (Table 2). We observed substantial heterogeneity when stratifying by invasive disease versus DCIS and therefore show these results separately. This association was stronger for DCIS [HR=1.23 (95% CI: 1.12, 1.35)] than for invasive breast cancer [HR=1.02 (95% CI: 0.96, 1.07)]. Similarly, PM2.5 (IQR=3.6μg/m3) was positively associated with DCIS incidence [HR=1.16 (95% CI: 1.02, 1.31)] but not invasive breast cancer [HR=1.03 (95% CI: 0.96, 1.09)]. No elevated HRs were observed in relation to PM10 (IQR=5.8μg/m3). Further adjustment for other known and established breast cancer risk factors and other markers of socioeconomic status, including household income, census-tract income, marital status, parity, and BMI, did not materially change the point estimates.

    Table 2 Air pollutants and risk of invasive breast cancer and DCIS, Sister Study, 2003–2009.

    Table 2 has eleven columns. The first column lists air pollutants. The adjacent columns list the number of cases and the values for age-adjusted HR (95 percent C I) and models 1 and 2 HR (95 percent C I) for overall breast cancer, invasive breast cancer, and DCIS.
    Air pollutantaOverall breast cancerInvasive breast cancerDCIS
    Cases (n)Age-adjusted HR (95% CI)Model 1 HR (95% CI)bModel 2 HR (95% CI)cCases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)cCases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)c
    PM2.52,8201.05 (1.00, 1.11)1.05 (0.99, 1.11)1.04 (0.98, 1.10)2,2061.03 (0.96, 1.09)1.02 (0.95, 1.08)6101.16 (1.02, 1.31)1.15 (1.02, 1.30)
    PM102,8201.01 (0.97, 1.05)1.01 (0.97, 1.05)1.01 (0.97, 1.05)2,2061.00 (0.96, 1.04)1.00 (0.95, 1.04)6101.06 (0.99, 1.15)1.06 (0.99, 1.15)
    NO22,8171.08 (1.03, 1.13)1.06 (1.02, 1.11)1.06 (1.01, 1.11)2,2031.02 (0.96, 1.07)1.01 (0.96, 1.07)6101.23 (1.12, 1.35)1.23 (1.12, 1.36)

    Note: CI, confidence interval; DCIS, ductal carcinoma in situ; HR, hazard ratio; IQR, interquartile range; NO2, nitrogen dioxide; PM2.5, particulate matter <2.5μm in aerodynamic diameter; PM10, particulate matter <10μm in aerodynamic diameter.

    aHR for a unit increase in the IQR difference:PM2.5=3.6μg/m3,PM10=5.8μg/m3, and NO2=5.8ppb.

    bAdjusted for age, race, education, smoking status, and postmenopausal hormone use.

    cAdjusted for age, race, education, income, census tract–level income, marital status, parity, smoking status, body mass index, and postmenopausal hormone use.

    An IQR increase in NO2 was inversely associated with ERPR breast cancer [HR=0.87 (95% CI: 0.73, 1.04)] but not with ER+PR+ breast cancer [HR=1.03 (95% CI: 0.95, 1.10)] (Table 3). Associations for PM2.5 and PM10 did not vary by ER/PR status of the tumor. We did not observe notable heterogeneity in the observed associations by menopausal status at diagnosis (see Table S1).

    Table 3 Air pollutants and risk of invasive ER+PR+ and ERPR breast cancer, Sister Study, 2003–2009.

    Table 3 has seven columns. The first column lists air pollutants. The adjacent columns list number of cases and values for models 1 and 2 HR (95 percent C I) for E R plus PR plus invasive and E R minus PR minus invasive.
    Air pollutantaER+PR+ invasiveERPR invasive
    Cases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)cCases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)c
    PM2.51,3471.01 (0.93, 1.10)1.00 (0.92, 1.08)2530.94 (0.78, 1.13)0.95 (0.79, 1.14)
    PM101,3471.00 (0.95, 1.06)1.00 (0.95, 1.06)2530.89 (0.78, 1.02)0.89 (0.78, 1.02)
    NO21,3461.03 (0.97, 1.11)1.03 (0.95, 1.10)2530.86 (0.72, 1.02)0.87 (0.73, 1.04)

    Note: CI, confidence interval; ER, estrogen receptor; HR, hazard ratio; IQR, interquartile range; NO2, nitrogen dioxide; PM2.5, particulate matter <2.5μm in aerodynamic diameter; PM10, particulate matter <10μm in aerodynamic diameter; PR, progesterone receptor.

    aHR for a unit increase in the IQR difference: PM2.5=3.6μg/m3, and PM10=5.8μg/m3, NO2=5.8ppb.

    bAdjusted for age, race, education, smoking status, and postmenopausal hormone use.

    cAdjusted for age, race, education, income, census tract–level income, marital status, parity, smoking status, body mass index, and postmenopausal hormone use.

    Associations for invasive breast cancer and exposure to PM2.5 (pheterogeneity=0.04), PM10 (pheterogeneity=0.04), and NO2 (pheterogeneity=0.05) all varied notably by geographic region (Table 4). An IQR increase in PM2.5 [HR=1.14 (95% CI: 1.02, 1.27)] was associated with invasive breast cancer in women residing in the West but not other geographic regions [Northeast HR=0.89 (95% CI: 0.73, 1.07); Midwest HR=0.93 (95% CI:0.81, 1.08), South HR=1.03 (95% CI: 0.90, 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 NO2 was similarly associated with breast cancer among women living in the West [HR=1.09 (95% CI: 0.99, 1.21)] as well as for women residing in the South [HR=1.16 (95% CI: 1.01, 1.33)]. For DCIS, in general we observed associations to be more pronounced in women living in the Northeast or the Midwest. For example, for an IQR increase in PM2.5, we observed an HR=1.35 (95% CI: 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 pattern was similar for PM10 (pheterogeneity=0.01). For NO2, risk of DCIS also varied by region (pheterogeneity=0.01), with the highest HRs observed in the Midwest [HR=1.73 (95% CI: 1.39, 2.14)]. These associations persisted with further covariate adjustment and when including PM2.5 component clusters in the model. These associations were also robust to the inclusion of additional interaction terms with cluster, BMI, and education in the model (see Table S2).

    Table 4 Air pollutants and risk of invasive breast cancer and DCIS by geographic region, Sister Study, 2003–2009.

    Table 4 has ten columns. The first column lists air pollutants. The adjacent columns list region; number of cases; and values for models 1, 2, and 3 H R (95 percent C I) for invasive breast cancer and DCIS.
    Air pollutantaInvasive breast cancerDCIS
    RegionCases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)cModel 3 HR (95% CI)dCases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)cModel 3 HR (95% CI)d
    PM2.5
    Northeast3450.89 (0.73, 1.07)0.86 (0.71, 1.04)0.79 (0.64, 0.99)1111.35 (0.97, 1.88)1.36 (0.97, 1.9)1.43 (0.97, 2.09)
    Midwest6090.93 (0.81, 1.08)0.89 (0.77, 1.04)0.91 (0.77, 1.07)1611.68 (1.21, 2.34)1.64 (1.17, 2.30)1.56 (1.08, 2.26)
    South7531.03 (0.90, 1.17)1.02 (0.89, 1.16)1.07 (0.90, 1.27)2001.07 (0.83, 1.39)1.08 (0.83, 1.40)1.09 (0.78, 1.52)
    West4991.14 (1.02, 1.27)1.12 (1.00, 1.26)1.21 (1.03, 1.43)1381.10 (0.89, 1.36)1.08 (0.86, 1.35)1.21 (0.89, 1.65)
    pheterogeneity0.040.030.030.070.070.3
    PM10
    Northeast3450.90 (0.79, 1.02)0.88 (0.77, 1.00)0.86 (0.76, 0.99)1111.13 (0.91, 1.41)1.15 (0.91, 1.44)1.14 (0.91, 1.44)
    Midwest6090.92 (0.82, 1.04)0.91 (0.80, 1.03)0.92 (0.80, 1.06)1611.55 (1.22, 1.96)1.55 (1.22, 1.97)1.46 (1.13, 1.90)
    South7530.91 (0.80, 1.03)0.91 (0.80, 1.03)0.91 (0.80, 1.04)2001.00 (0.79, 1.28)1.02 (0.80, 1.30)1.07 (0.82, 1.38)
    West4991.04 (0.98, 1.10)1.04 (0.98, 1.10)1.04 (0.98, 1.11)1381.01 (0.90, 1.13)1.01 (0.90, 1.13)1.00 (0.89, 1.12)
    pheterogeneity0.040.040.070.010.010.02
    NO2
    Northeast3450.92 (0.82, 1.03)0.89 (0.79, 1.01)0.89 (0.79, 1.01)1111.16 (0.97, 1.39)1.19 (0.98, 1.44)1.19 (0.98, 1.44)
    Midwest6091.00 (0.88, 1.14)0.97 (0.85, 1.11)0.99 (0.86, 1.15)1611.73 (1.39, 2.14)1.72 (1.38, 2.15)1.69 (1.33, 2.14)
    South7501.16 (1.01, 1.33)1.18 (1.03, 1.37)1.20 (1.02, 1.41)2001.12 (0.86, 1.45)1.14 (0.87, 1.50)1.04 (0.75, 1.42)
    West4991.09 (0.99, 1.21)1.09 (0.98, 1.20)1.13 (1.00, 1.26)1381.17 (0.97, 1.41)1.16 (0.95, 1.41)1.14 (0.91, 1.41)
    pheterogeneity0.050.060.040.010.010.01

    Note: CI, confidence interval; DCIS, ductal carcinoma in situ; HR, hazard ratio; IQR, interquartile range; NO2, nitrogen dioxide; PM2.5, particulate matter <2.5μm in aerodynamic diameter; PM10, particulate matter <10μm in aerodynamic diameter.

    aHR for a unit increase in the IQR difference: PM2.5=3.6μg/m3, PM10=5.8μg/m3, and NO2=5.8ppb.

    bAdjusted for age, race, education, smoking status, and postmenopausal hormone use.

    cAdjusted for age, race, education, income, census tract–level income, marital status, parity, smoking status, body mass index, and postmenopausal hormone use.

    dAdjusted for age, race, education, income, census tract–level income, marital status, parity, smoking status, body mass index, postmenopausal hormone use, and PM2.5 component clusters.

    Overall, the associations for PM2.5 using 2010 air pollution estimates (2010IQR=2.9μg/m3) were similar to those from our main results using data from 2006 [e.g., 2010 invasive HR=1.01 (95% CI: 0.95, 1.07) vs. 2006 invasive HR=1.03 (95% CI: 0.96, 1.09)] (Table 5). Consistent with the results stratified by geographic region, invasive breast cancer risk also varied by PM2.5 component cluster (pheterogeneity=0.3) (Table 5). Specifically, we observed an elevated risk of invasive breast cancer associated with PM2.5 exposure for both Cluster 4 (California; Figure 1) and Cluster 7 (West; Figure 1) but no increase in risk for women in any of the other clusters. The California monitors were captured in Cluster 4 (Figure 1), which was characterized by having low S fractions and large fractions of Na and NO3 (Figure 2), indicating exposure to marine aerosols and agricultural emissions (Keller et al. 2017). For an IQR increase in PM2.5 for women who were assigned to Cluster 4, we observed a 25% higher risk of invasive breast cancer [HR=1.25 (95% CI: 0.97, 1.60)]. Cluster 7 was also centered in the Western United States (Figure 1), and was defined by high fractions of Si, Ca, K, and Al (Figure 2), consistent with the surface soil in this geographic region (Shacklette and Boerngen 1984). For women in Cluster 7, we also observed an elevated risk associated with an IQR increase in PM2.5 [HR=1.60 (95% CI: 0.90, 2.85)], but the estimate for this cluster was imprecise due to the small number of cases (n=59). These associations remained similar with further adjustment for additional covariates and inclusion of geographic region in the adjustment set.

    Table 5 PM2.5, k-means clusters, and risk of invasive breast cancer and DCIS, Sister Study, 2003–2009.

    Table 5 has nine columns. The first column lists air pollutants. The adjacent columns list number of cases and values for models 1, 2, and 3 H R (95 percent C I) for invasive breast cancer and DCIS.
    Air pollutantaInvasive breast cancerDCIS
    Cases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)cModel 3 HR (95% CI)dCases (n)Model 1 HR (95% CI)bModel 2 HR (95% CI)cModel 3 HR (95% CI)d
    2010 PM2.52,2061.01 (0.95, 1.07)1.00 (0.94, 1.06)6101.14 (1.01, 1.27)1.13 (1.01, 1.27)
    By clusterse
     16070.95 (0.81, 1.10)0.93 (0.80, 1.09)0.93 (0.80, 1.09)1861.38 (1.02, 1.86)1.38 (1.02, 1.85)1.38 (1.02, 1.86)
     26490.99 (0.86, 1.14)0.96 (0.83, 1.12)0.95 (0.79, 1.13)1691.37 (1.03, 1.83)1.30 (0.97, 1.76)1.28 (0.9, 1.81)
     34380.95 (0.74, 1.22)0.96 (0.75, 1.24)0.95 (0.73, 1.23)1131.22 (0.75, 1.96)1.27 (0.78, 2.07)1.27 (0.77, 2.09)
     42031.25 (0.97, 1.60)1.24 (0.96, 1.60)1.24 (0.96, 1.60)491.33 (0.80, 2.22)1.32 (0.79, 2.21)1.32 (0.79, 2.21)
     52031.00 (0.74, 1.36)1.04 (0.77, 1.42)1.05 (0.77, 1.42)621.18 (0.67, 2.07)1.31 (0.74, 2.32)1.31 (0.74, 2.32)
     6470.82 (0.36, 1.87)0.95 (0.40, 2.29)0.97 (0.40, 2.37)171.22 (0.35, 4.26)1.32 (0.34, 5.14)1.36 (0.34, 5.47)
     7591.60 (0.90, 2.85)1.66 (0.92, 2.99)1.71 (0.93, 3.14)220.97 (0.40, 2.38)0.81 (0.32, 2.04)0.88 (0.34, 2.23)
    pheterogeneity0.30.30.30.90.90.9

    Note: —, not applicable; CI, confidence interval; DCIS, ductal carcinoma in situ; HR, hazard ratio; IQR, interquartile range; NO2, nitrogen dioxide; PM2.5, particulate matter <2.5μm in aerodynamic diameter; PM10, particulate matter <10μm in aerodynamic diameter.

    aHR for a unit increase in the IQR difference: PM2.5=2.9μg/m3, PM10=5.8μg/m3, and NO2=5.8ppb.

    bAdjusted for age, race, education, smoking status, and postmenopausal hormone use.

    cAdjusted for age, race, education, income, census tract–level income, marital status, parity, smoking status, body mass index, and postmenopausal hormone use.

    dAdjusted for age, race, education, income, census tract–level income, marital status, parity, smoking status, body mass index, postmenopausal hormone use, and geographic region.

    eCluster locations are provided in Figure 1.

    Figure 2 plots the log (species fraction) standardized scale (ranging from negative 2 to 2 in unit increments) (y-axis) for clusters 1 to 7 across component data (C o, N i, V, S e, A s, Cr, M n, B r, C u, C d, Z n, A l, C a, N a, K, F e, S i, E C, S, N O 3, S O 4, and O C) (x-axis).

    Figure 2. Relative composition by PM2.5 clusters. Clusters were identified using predictive k-means in the 2010 annual average PM2.5 component data. Species mass fractions were log transformed and then standardized. EC, elemental carbon; NO3, nitrate; OC, organic carbon; PM2.5, particulate matter <2.5μm in aerodynamic diameter; SO4, sulfate.

    For DCIS, although sample sizes were small, there was less evidence of risk heterogeneity by cluster (pheterogeneity=0.9) (Table 5). Across the clusters, PM2.5 was positively associated with DCIS in all but Cluster 7. For example, a higher risk of DCIS in relation to an IQR increase in PM2.5 was observed for women in Cluster 1 [HR=1.38 (95% CI: 1.02, 1.86)] and Cluster 2 [HR=1.37 (95% CI: 1.03, 1.83)]. Cluster 1 is in the Midwest and Mid-Atlantic region (Figure 1) with above-average NO3 and SO42 (Figure 2), which is consistent with high ambient ammonia levels from agriculture. Cluster 2 is in the Northeast (Figure 1) and is characterized by higher fractions of Cd, V, and Ni (Figure 2). Elevated HRs, but with wide CIs, were also observed for women in Cluster 3 [HR=1.22 (95% CI: 0.75, 1.96)], Cluster 4 [HR=1.33 (95% CI: 0.80, 2.22)], Cluster 5 [HR=1.18 (95% CI: 0.67, 2.07)], and Cluster 6 [HR=1.22 (95% CI: 0.35, 4.26)] in relation 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 (see Table S3). However, we did note an elevated HR for invasive breast cancer was observed for PM2.5 in women who lived in their residences for 10y [HR=1.07 (95% CI: 0.98, 1.17)]. We observed modification by obesity; women who had a BMI 30kg/m2 had a higher risk of invasive breast cancer associated with PM2.5 [HR=1.19 (95% CI:1.06, 1.34), pheterogeneity=0.02], and NO2 [HR=1.11 (95% CI: 1.01, 1.21), pheterogeneity=0.1] (see Table S4). We observed no significant effect measure modification of the associations for air pollutants and breast cancer risk by extent of breast cancer family history or hormone therapy use (see Tables S5 and S6). As expected, there was substantial overlap between clusters and geographic region (see Table S7).

    Discussion

    In this large, U.S.-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 PM2.5 tended to be related to invasive breast cancer risk in the Western United States, whereas for DCIS, the associations were most evident among women in the Northeast and Midwest. These results were consistent with our analysis utilizing predictive k-means clustering to evaluate PM2.5 component mixtures in relation to breast cancer risk. PM2.5 levels in two Western-based clusters were related to the risk of invasive breast cancer, whereas PM2.5 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 U.S.-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 mixture containing numerous carcinogens and endocrine disruptors (Loomis et al. 2013). In breast cancer cell lines, PM has been shown to have estrogenic properties and oxidative stress–related DNA-damaging activity (Chen et al. 2013). Inhaled toxicants can reach the breast tissue (Hill and Wynder 1979) and traffic-related air pollution has been associated with aberrant DNA methylation in breast cancer–related genes measured in tumor tissue (White et al. 2016). Air pollution has also been related to higher breast density (DuPre et al. 2017; White et al. 2019c; Yaghjyan et al. 2017), a marker of breast cancer risk.

    Markers of traffic pollution such as NO2, NOx, 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; Nie et al. 2007; Reding et al. 2015), whereas results for measures of PM have been mostly null (Andersen et al. 2017a, 2017b; Hart et al. 2016; Reding et al. 2015; Villeneuve et al. 2018). However, these studies have largely not considered the impact of geographic variability or PM heterogeneity. For example, although we too saw little consistent evidence of an association with PM2.5 or PM10 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. (2017b) considered PM components individually in relation to postmenopausal breast cancer risk. They observed a higher breast cancer risk for exposure to both PM2.5 and PM10 V and PM10 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 k-means clustering, which is a data reduction technique that identifies subgroups of individuals who are exposed to similar combinations of PM components. This permits the identification of PM component mixtures and consideration of how these complex mixtures influence the association between PM2.5 and breast cancer risk.

    We observed heterogeneity by geographic region and PM2.5 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 living in the Northeast. In our study, PM2.5 was related to DCIS across most of the clusters despite lower power to detect associations. In contrast, PM2.5 was associated with invasive breast cancer only in women assigned to two Western-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 NO3, indicative of marine aerosols and agricultural emissions. Airborne exposure to pesticides from agricultural practices may contribute to cancer risk (Engel et al. 2005; Lee et al. 2002; Lerro et al. 2015). Cluster 7, which was more widely spread across the Western United States, had high fractions of Si, Ca, K, and Al, consistent with the surface soil in this region (Shacklette and Boerngen 1984). In a subset of our study population with DNA methylation data, among women in Clusters 4 and 7, PM2.5 was also associated with DNA methylation-based biologic age acceleration (White et al. 2019a), a marker of future breast cancer risk (Kresovich et al. 2019). These consistent findings support a role for these clusters of PM2.5 components in breast carcinogenesis.

    Differences between overall results for invasive breast cancer and DCIS were 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 DCIS, which is primarily detected by screening (Virnig et al. 2010). To address this, we further adjusted our models for risk of DCIS for individual and census tract–level 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 driving the differences in DCIS/invasive disease risk by region. Another potential explanation is that these mixtures of pollutants simply contribute differently to breast cancer risk by stage of disease, perhaps by influencing tumor 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 pollution was similarly associated with DCIS (Mordukhovich et al. 2016).

    We did not observe substantial evidence of variability in the associations of overall air pollutant exposures and breast cancer risk by menopausal status or by tumor subtype. However, a limitation 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 PM2.5 and NO2 was higher among women with a BMI 30kg/m2, suggesting a possible synergistic relationship between obesity and air pollution. Components of air pollution, such as PAHs, are lipophilic (IARC 2010), whereas other components, such as metals, have been detected in visceral fat (Qin 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 (Niehoff et al. 2017) and airborne metals (White et al. 2019b).

    A strength of this study was the use of predictive k-means clustering to determine subgroups of women who were exposed to different PM2.5 component mixtures. Consideration of the mixture is important because PM is not a homogenous exposure and our approach permitted a more refined and nuanced exposure assessment. The predictive k-means approach used to identify and assign PM component clusters in the Sister Study was an unsupervised method, meaning that the clusters identified are useful for a public health-focused approach to identify existing air pollution mixtures and determine how they are related to health 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 PM2.5 components, it is possible that these clusters may be influenced by other correlated unmeasured air pollutants. In addition, the accuracy of the concentration measurements may vary for some of the PM2.5 components and thus may result in differential measurement error. Furthermore, we classified individuals into the cluster 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 enrollment residence. However, a limitation of this approach is that we used air pollution measures estimated around the time of enrollment in the study (on average, 8 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 10y at their enrollment address. It is possible that more long-term exposure, or exposure occurring during hypothesized susceptible windows of exposure including childhood (Bonner et al. 2005; Nie et al. 2007; Shmuel et al. 2017), or exposure during the reproductive time period may be more relevant.

    In conclusion, in this large, prospective U.S.-wide cohort, we observed that measures of air pollution, including NO2, PM2.5, and PM10, were related to both invasive and DCIS breast cancer when stratifying by geographic region. Using predictive k-means clusters to consider the potential modifying role of PM2.5 components, we observed that the risk of breast cancer varied based on PM2.5 component clusters, which were also correlated 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 mixture, as well as by tumor staging, when assessing associated risks with breast cancer.

    Acknowledgments

    This research was supported by the Intramural Research Program of the National Institute of Environmental Health Sciences, National Institutes of Health (Z01-ES044005 and ES103332-01).

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    The authors declare they have no actual or potential competing financial interests.