Air pollution Exposure And Mammographic Breast Density In Tehran, Iran

doi:10.21203/rs.3.rs-988346/v1

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Air pollution Exposure And Mammographic Breast Density In Tehran, Iran

https://doi.org/10.21203/rs.3.rs-988346/v1

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Background: Air pollution is one of the major public health challenges in many parts of the world possibly has an association with breast cancer. However, the mechanism is still unclear. This study aimed to find an association between exposure to six criteria ambient air pollutants (PM_2.5, PM₁₀, SO₂, NO₂, O₃, and CO) and mammographic breast density (MBD), as one of the strongest predictors for developing breast cancer, in women living in Tehran, Iran.

Methods: Participants were selected from women attending two university hospitals for screening mammography from 2019 to2021. Breast density was rated by two expert radiologists. Individual exposures to 3-years ambient air pollution levels at the residence were estimated.

Results: The final analysis in 791 eligible women showed that low and high breast density was detected in 34.8% and 62.2% of participants, respectively. Logistic regression analysis after considering all possible confounding factors represented that an increase in 10 units of NO₂ (ppb) exposure was associated with an increased risk of breast density with an OR equal to 1.47 (95% CI: 1.10 to 1.97). Furthermore, CO level was associated with a decreasing breast density (OR = 0.33, 95% CI = 0.17 to 0.64). None of the other pollutants were associated with breast density.

Conclusion: MBD was associated with higher levels of ambient air NO₂ and lower levels of CO. Perhaps MBD monitoring as an available tool in each population, can help the prediction of future breast cancer occurrence and finding the high-risk geographic areas.

Environmental Policy

Breast Density

Mammography

Air Pollutants

Nitrogen Dioxide

Carbon monoxide.

Air pollution is one of the major public health challenges in many parts of the world and in large cities. Ambient air pollution is a mixture of different pollutants originating from natural and anthropogenic sources and the International Agency for Research on Cancer (IARC) classified it as Group 1 carcinogenic to humans (1). Several studies showed that short-and log-term exposure to air pollution can cause many chronic and acute health effects. Numerous studies reported that long-term exposure to outdoor air pollution caused globally around 4.24 million premature deaths annually (2–5).

Breast cancer is one of the worldwide leading causes of mortality and morbidity, and according to a report of GLOBOCAN in 2018 accounts for more than 11.6% of all female cancers; while the disease burden of breast cancer shows an increasing trend in some populations (6). Because this disease imposes a heavy burden on the health system, more preventive efforts are necessary and further investigation should explore the underlying reasons for these epidemiological trends. Ecologic studies suggest that breast cancer risk is elevated in urban areas with high levels of air pollution compared to rural areas (7, 8). Air pollution contains many carcinogens and other compounds that may act as endocrine disruptors, and air pollution exposure has been globally linked to many cancers such as lung, breast, and bladder cancer (9). In 1979 Hill and Winder found that inhaled toxicants (nicotine and cotinine) were detectable in breast fluid after 30 minutes of smoking (10). Thus, toxic chemicals can reach the breast tissue and have possibly some impacts on it.

A systematic review and meta-analysis in 2021 investigated whether high levels of air pollution exposure were related to increased breast cancer risk (11). This study showed that NO₂ had a "moderate level of evidence" and that PM_2.5 and PM₁₀ have an "inadequate level of evidence" for supporting their association with breast cancer risk. Also, the biological mechanism of the effects of air pollutants on breast cancer still remains unknown (11).

Mammographic breast density (MBD) is one of the strongest predictors and biomarkers for developing breast cancer (12). Growing evidence suggests that environmental risk factors such as xenoestrogens [(Bisphenol-A (BPA)] and metalloestrogens (lead, cobalt, and magnesium) have a direct association with MBD (13, 14). However, one study has reported a reverse association between serum levels of Polychlorinated biphenyl (PCBs) as a xenoestrogen and MBD (15). Limited studies evaluated the association between MBD and air pollution exposure, which had also inconsistent results (16–19). To draw risk-reducing strategies for breast cancer, studying the impacts of ambient air pollutants on breast density may provide valuable data for monitoring and etiologic factors. Previous studies had limitations in the assessment of the exposure, in adjusting confounding variables, and in outcome ascertainment to find a causal relationship; therefore further studies have been recommended (11).

Tehran as the capital of Iran is a megacity with about 10 million residents and air pollution is a major environmental challenge in this city. The people of Tehran are more exposed to high levels of ambient air pollution, to the point where government and non-government offices are sometimes closed due to the severity of air pollution (3, 20). Therefore, the present study was designed to investigate whether there is an association between exposure to six criteria ambient air pollutants (Nitrogen dioxide (NO₂), Sulfur dioxide (SO₂), Carbon monoxide (CO), Ozone (O₃), and Particulate matter (PM) _2.5, ₁₀) and MBD in women living in Tehran, Iran.

Study design and participants

This study was designed as a cross-sectional study; participants were selected from women attending two university hospitals affiliated to Tehran University of Medical Sciences, Tehran, Iran, for screening mammography from 2019 to 2021. The study was approved by the ethics committee of Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1398.897), and all participants have signed an informed consent. All methods have been performed in accordance with the relevant principles of the Declaration of Helsinki.

Criteria for inclusion in the study were at least 3 years residency in the capital city of Iran (Tehran) and having the ability to fill questionnaires. Exclusion criteria included suspicion for malignancy in the current mammography and an imprecise address.

Data Collection

Participants were asked to fill a questionnaire that captured demographic information, self-reported age, weight, height, reproductive history, menopause status, smoking history (active and passive), history of oral contraceptive (OCP) use, current use of hormone replacement therapy, and familial history of breast and ovarian cancer. All women who either had a current or previous history of active and passive (second hand) smoking were defined as having a positive exposure to smoke. Menopause was defined as cessation of the menstrual period at least one year sooner, women were stratified into premenopausal and postmenopausal status. Furthermore, we gathered information about current aspirin and metformin use and consumption duration in each woman. Routine use of supplements including vitamin D, calcium, Vitamin E, Omega 3, and Evening Primrose oil were also recorded.

One expert radiologist reported the breast density in each center. In order to decrease both intra- and interreader variability, the mammographic breast density was checked by a third radiologist. Radiologists rated MBD according to the American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) classification into four categories: almost entirely fatty (BI-RADS 1), scattered areas of fibro glandular density (BI-RADS 2), heterogeneously dense (BI-RADS 3), and extremely dense (BI-RADS 4) (21). We categorized MBD into low density (ACR 1 and 2) and high density (ACR 3 and 4).

The exact address of residence of the participants in the recent 3 years and the telephone number of that place were recorded. Also, in the employed women, the address of their place of work and the hours of their attendance to work were recorded.

Air pollution exposure assessment

In this study, estimating the exposure of participants to ambient criteria air pollution was done in the following three steps:

1. Outdoor air quality data gathering from fixed monitoring stations belong to Tehran Air Quality Control Company.

2. Data cleaning of air quality monitoring stations in order to outlier data detection.

3. Individual long-term exposure assessment using air quality data and inverse distance weighting (IDW) approach.

Air quality data gathering

Real time hourly ambient air quality in Tehran city is monitored by fixed monitoring stations. In Tehran city, Air Quality Control Company (AQCC) affiliated to the Tehran Municipality is responsible for monitoring criteria air pollutant (PM_2.5, PM₁₀, NO₂, O₃, SO₂, and CO). At the time of this study in 2020, there were 22 monitoring stations in Tehran belonged to the AQCC. Considering that the data of the AQCC stations are available on an hourly basis and are publicly available online, and that these monitoring stations are spatially located in all districts of Tehran city, therefore we used the data obtained from air quality monitoring stations belonged to AQCC in this study.

Finally, hourly data of six outdoor criteria air pollutants for the 3-years residency of participants were obtained from the website of AQCC (Available at: http: // airnow .tehran.ir / home / DataArchive.aspx).

Air quality data processing

Data quality control is the most important part of air quality studies and estimating health effects. Data quality assurance was performed according to international organization guidelines such as World Health Organization (WHO), Environmental Protection Agency (EPA), and the European Union (22–24). Due to numerous operational and calibration problems related to air pollutant measuring stations, outlier detection and data cleaning from monitoring stations is very important and the results would have insufficient scientific validity if this step is omitted.

In the present study, first the data of all monitoring stations were obtained and then hourly data coverage of each pollutant in each station during the three years was determined. Included monitoring stations were only stations with ≥75% completeness of the total hours during the study period (22, 24). Then, in order to outlier data detection, the modified Z-score approach proposed by some researchers for this purpose was used (3, 23, 25, 26). Briefly, in order to identify outlier data, the following steps were used:

Calculating the Z score for each hourly data at each station using the following equation:

$$Z=\frac{{\left(Concentration\right)}_{Hourly}-{\left(Concentration\right)}_{Annual}}{SD of annual concentrations}$$

Calculating the following four conditions:

$${Z}_{1}=\left|Z\right|>4$$

$${Z}_{2}=({Z}_{t}-{Z}_{t-1})>6$$

${Z}_{3}=(\frac{{Z}_{t}}{{RM3 \left({Z}_{t}\right)}_{}}$ >1.5

${Z}_{4}=\frac{({Z}_{t}-{Z}_{t-1})}{City({Z}_{t}-{Z}_{t-1})}$ >2

Finally, the air quality data were detected as an outlier data and removed if they meet four above-mentioned conditions.

Individual long-term exposure assessment

To determine the long-term exposure of each participant to ambient air pollutants, the exact address according to the area, place, street, and alley in each year was obtained. In working women, if the place of work and living were different, the area and time spent in that area were also considered. Then, according to the location of monitoring stations and the location of the study subject, three of the nearest included air quality monitoring stations were identified for each participant and using the average annual data and IDW method, the 3-year annual mean of exposure was estimated as long-term exposure for each study subject.

Statistical analysis

Data were presented with mean ± standard deviation (SD) for continuous and frequency (percentage) for categorical variables. The ANOVA, t-test, and chi-square test were used to compare variables between study groups in the simple analysis step. A multiple logistic regression analysis was done between all pollutants criteria as independent variables with breast density as a dichotomous dependent variable (low = 0 and high =1). This analysis was done via two non-stepwise and stepwise algorithms and the result of the stepwise algorithm was chosen as significant pollution criteria. After that, another logistic regression analysis was done to evaluate whether the effects of pollutants on breast density were independent or affected by potential confounding variables. This analysis was done as three models. For the first one, only significant pollutants were considered in the model. In the second model, medical and demographic variables were considered in addition to pollutants. In the last model, the history of medicine and supplement use was added to the previous variables. In the logistic regression analysis, odds ratio (OR) with 95% confidence interval (CI) was reported in addition to the p-value. All calculations were performed in IBM SPSS (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0, Armonk, NY: IBM Corp) and the charts were drawn with MS Excel (Microsoft Co., Redmond, WA, USA). P-value < 0.05 was considered significant.

Based on inclusion criteria 813 women were screened in this study. We excluded participants who were suspicious for malignancy in the current mammography (n=14), and who had written an incomplete address (n=8); finally 791 eligible women were recruited.

The mean age was 50.14 ± 7.61 (38-80) years old. About half of the women (50.1%) were premenopausal, and half of them were menopause (49.9%) at time of recruitment in the study. In the mammographies, low breast density was reported in 34.8% (n = 299) and high breast density in 62.2% (n = 492). Table 1 compares general and reproductive factors and other variables between breast density categories. As shown, all variables except the age of menarche and the whole breastfeeding duration had a statistically significant difference between two groups of breast density (P-value <0.05). The comparison between the 4 categories of MBD is presented in supplementary table 1.

Table 1

Demographic, medical and drug history of women with high and low mammographic breast density
Variables		Low density	High density	P-value
Age (years)		53.25 ± 8.29	48.25 ± 6.47	<0.001
Body mass index (Kg/m²)		29.80 ± 5.35	27.19 ± 4.16	<0.001
Age of menarche (years)		13.69 ±1.57	13.51 ± 1.49	0.359
Age at first birth (years)		21.32± 5.22	22.46 ± 5.32	0.006
Parity (count)		2.59 ± 1.58	1.96 ± 1.30	<0.001
Breastfeeding duration (months)		35.14 ± 32.48	32.76 ± 29.30	0.290
Menopause	No	93 (23.5%)	303 (76.5%)	<0.001
Menopause	Yes	206 (52.2%)	189 (47.8%)	<0.001
History of OCP	No	160 (32.6%)	331 (67.4%)	<0.001
History of OCP	Yes	139 (46.3%)	161 (53.7%)	<0.001
Smoking	No	261 (36.5%)	455(63.5%)	0.016
Smoking	Active or passive	38 (50.7%)	37 (49.3%)	0.016
Occupation	Housewife	264 (39.3%)	407 (60.7%)	0.008
	Employed	20 (23%)	67 (77%)
	Retired	15 (45.5%)	18 (54.5%)
Metformin	No	250 (35.7%)	450 (64.3%)	0.001
Metformin	Yes	49 (53.8%)	42 (46.2%)	0.001
Aspirin	No	239 (34.9%)	446 (65.1%)	<0.001
Aspirin	Yes	60 (56.6%)	46 (43.4%)	<0.001
Calcium	No	143 (32.5%)	297 (67.5%)	0.001
Calcium	Yes	156 (44.4%)	195 (55.6%)	0.001
Vitamin D	No	164 (41.2%)	234 (58.8%)	0.047
Vitamin D	Yes	135 (34.4%)	258 (65.6%)	0.047
Vitamin E	No	247 (38.3%)	398 (61.7%)	0.547
Vitamin E	Yes	52 (35.6%)	94 (64.4%)	0.547
Evening Primrose oil	No	287 (38.2%)	465 (61.8%)	0.353
Evening Primrose oil	Yes	12 (30.8%)	27 (69.2%)	0.353
Omega-3	No	264 (37.9%)	432 (62.1%)	0.896
Omega-3	Yes	35 (37.2%)	59 (62.8%)	0.896
History of Breast Disease	No	217 (35.6%)	392 (64.4%)	0.021
History of Breast Disease	Yes	82 (45.1%)	100 (54.9%)	0.021
Percent present between categories of each variable. P-values were computed with t-test for continuous and chi-square test for categorical variables.

In the first step, in a univariate analysis using a t-test, all six pollutants criteria were compared between low and high breast density. In this comparison, except for ambient air CO, which was on the borderline statistically significant (P-value = 0.054), other variables were not significantly different between the two groups (Figure 1 & Supplementary table 2). Due to an unclear trend of ambient criteria air pollutants between the four categories of MBD, the comparison has been conducted only between high and low breast densities; and the comparison between the 4 categories is presented in the supplementary table 3.

Unlike univariate analysis, multiple regression analysis between six ambient air pollutants and MBD showed that outdoor air NO₂ (P-value = 0.003) and CO (P-value = 0.001) had a significant relationship with breast density. Logistic regression analysis with stepwise algorithm and breast density as a dependent variable showed that an increase in each unit of NO₂ (ppb) exposure was associated with an increased risk of breast density with an OR equal to 1.04 (95% CI: 1.01 to 1.07); and an OR equal to 1.47 for each 10 unit increase in NO₂. Furthermore, CO level was associated with a decreasing risk of breast density in each 1 ppm (OR = 0.33, 95% CI = 0.17 to 0.64). None of the other pollutants were associated with breast density (Table 2).

Table 2

Stepwise and Non-stepwise logistic regression for criteria air pollutants with possible impact on mammographic breast density
	Non-stepwise algorithm				Stepwise algorithm
	P-value	OR	95%C.I Lower	95%C.I Upper	P-value	OR	95%C.I Lower	95%C.I Upper
CO (ppm)	0.024	0.429	0.206	0.893	0.001	0.331	0.172	0.637
NO2 (ppb)	0.055	1.045	0.999	1.093	0.003	1.039	1.013	1.066
O3 (ppb)	0.870	0.988	0.852	1.145
SO2 (ppb)	0.605	0.926	0.692	1.240
PM10 (µg/m³)	0.378	0.944	0.829	1.073
PM2.5 (µg/m³)	0.305	1.129	0.896	1.422
Constant	0.000	1.754		--	0.000	1.758	--
OR = Odds ratio, C.I = Confidence interval, NO₂= Nitrogen dioxide (NO₂), SO₂ = Sulfur dioxide, CO = Carbon monoxide, O₃ = Ozone, PM = Particulate matter.

In order to evaluate whether the effects of pollutants on MBD is independent or disappears under the influences of confounding variables, two others multiple analyses were performed. In the first model, basic and reproductive factors (age, body mass index (BMI), Smoking, history of OCP usage, parity, menopause, and history of breast disease) were entered into the model. In the second model, metformin and aspirin intake, vitamin D, and calcium consumption were also entered into the model. Table 3 illustrates the results of the three models. Finally, multiple logistic regression analysis showed that ambient air CO (P = 0.018) and NO₂ (P = 0.022) had independent effects on the breast density.

Table 3

Three multiple logistic regression models for the effect of ambient air pollutants on mammographic breast density considering confounder variables
		P-value	OR	95% C.I. for OR
		P-value	OR	Lower	Upper
Model 1	CO	0.001	0.331	0.172	0.637
	NO2	0.003	1.039	1.013	1.066
	Constant	<0.001	1.758
Model 2	CO	0.020	0.411	0.195	0.868
	NO2	0.026	1.034	1.004	1.064
	Age	0.000	0.942	0.915	0.971
	BMI	0.000	0.884	0.851	0.919
	Smoking	0.063	0.602	0.353	1.029
	History of OCP	0.000	0.526	0.375	0.738
	Menopause	0.002	0.522	0.348	0.782
	Parity	0.708	0.975	0.856	1.111
	History of Breast disease	0.106	0.728	0.495	1.070
	Constant	<0.001	2454.3
Model 3	CO	0.018	0.404	0.190	0.856
	NO2	0.022	1.035	1.005	1.066
	Age	0.000	0.947	0.919	0.976
	BMI	0.000	0.884	0.851	0.920
	Smoking	0.088	0.624	0.363	1.072
	History of OCP	0.000	0.536	0.381	0.753
	Menopause	0.004	0.544	0.360	0.823
	Parity	0.938	0.995	0.872	1.135
	History of Breast disease	0.124	0.735	0.496	1.088
	Metformin	0.726	0.913	0.547	1.524
	Aspirin	0.070	0.641	0.397	1.037
	Vitamin D	0.040	1.426	1.016	2.001
	Calcium	0.203	0.795	0.558	1.132
	Constant	<0.001	1710.8
OR = Odds ratio, C.I = Confidence interval, NO₂= Nitrogen dioxide (NO₂), SO₂ = Sulfur dioxide, CO = Carbon monoxide, O₃ = Ozone, PM = Particulate matter.

In addition, a separate analysis was performed considering menopausal status. The relationship between ambient air NO₂ (OR= 1.07, 95% CI: 1.003- 1.139, P-value= 0.041) and CO (OR= 0.34. 95% CI: 0.120- 0.955, P-value=0.041) with MBD was observed only in menopausal women in the same direction as stated. In premenopausal women, breast density was not associated with ambient air pollutants (Supplementary table 4).

The comparison in breast density in women who live a lifetime in Tehran and other women didn't show any significant difference (data not shown in table).

The present study has evaluated the precise impact of long-term exposure to six criteria ambient air pollutants on MBD in Iranian women for the first time. Actually, many known and unknown factors are involved in breast tissue changes and eventually in breast cancer and it's not possible to control all confounding factors in a single context. By the way, based on the available evidence, we tried to evaluate the effects of six criteria ambient air pollutants on breast density considering the factors that seem to have an impact on MBD (basic and reproductive factors, aspirin, metformin, and supplement intake). To the best of our knowledge, there is no study with this broad level of assessment.

Our results represented that outdoor air NO₂ and CO exposure had statistically significant impacts on MBD. We found that an increased level of NO_2, as a marker of traffic-related air pollution (27), is associated with a higher MBD. Furthermore, ambient air CO concentration was associated with a lower MBD, while other criteria air pollutants were not related to MBD.

Our present results about ambient air NO₂ and PMx (PM_2.5 & PM₁₀) concentration were consistent with a recent systematic study and meta-analysis that found an increased risk of breast cancer with an increase in each 10 unit in NO₂ exposure (Hazard ratio (HR) = 1.02, 95% CI = 1.01-1.04), while PM_2.5 and PM₁₀ revealed no statistically significant associations with breast cancer risk (11). The results of our study on the relationship between air pollutants and MBD seem to be in line with studies that have examined the relationship between these pollutants and breast cancer.

Limited studies have evaluated the association between criteria ambient air pollutants and MBD with inconsistent results (16, 17, 19). Similar to our study, Du Pre and their colleague's results in the Nurses' Health Study couldn't support that recent particulate matter (PM_2.5, PM air_2.5−10, PM₁₀) or roadway exposure influenced breast density (16). Two other studies had contradictory results with the present study (17, 19). The Danish Diet, Cancer and Health Cohort investigated the association between long-term exposure to traffic-related air pollution (NO₂, NO_x) and MBD in a prospective cohort of women aged 50 and older. They found a reverse association between air NO₂ level and MBD (OR= 0.89, 95% CI: 0.80-0.89 per 10 µg/m³) with no interaction with menopause, smoking, or obesity (17). In Yaghjyan et al. study, women higher than 40 years old with known residential zip codes and estimated PM_2.5 and O₃ levels for the year preceding the mammogram date were included. They found that women with extreme breast density had higher mean PM_2.5 and lower O₃ exposure levels (19).

Numerous studies in line with our study have investigated the relationship between endocrine-disrupting chemicals (EDCs) and heavy metals with MBD (13, 14, 18). In a cross-sectional study in 725 women (40-65 years old), a higher urinary level of magnesium was associated with a higher MBD (13). In postmenopausal women (n = 264), women with high serum levels of BPA and mono-ethyl phthalate had an elevated breast density (14). In a large-scale study (n= 222,581), the relation of the MBD of women who underwent a routine screening mammogram in 2011 and residential levels of ambient air polycyclic aromatic hydrocarbons (PAHs) and metals was assessed. Higher residential levels of arsenic, cobalt, lead, manganese, nickel, or PAHs were individually associated with breast density. Comparing the highest to the lowest quartile, higher odds for dense breasts were observed for cobalt (OR = 1.60, 95% CI 1.56–1.64) and lead (OR = 1.56, 95% CI 1.52–1.64). These associations were stronger in premenopausal women (18). An exception is one cross-sectional study of PCBs, which reported some PCB congeners' plasma levels were associated with lower MBD in postmenopausal women (15).

Consistent with the present study, a review study by White and colleagues that summarized eight case-control studies and nine cohort studies suggested little evidence to support an association between particulate matter and breast cancer risk. More consistent findings have reported a relation between NO₂ or NO_X level and breast cancer (28).

In this study, we found a reverse association between CO level and MBD. Two recent studies that evaluated the effect of ambient air CO on breast cancer had equivocal results. A Korean study reported that CO concentration was positively and significantly associated with breast cancer (OR= 1.08, 95% CI= 1.06-1.10) (29) and another cohort study in Taiwan found that women who had CO poisoning were at a lower risk of developing breast cancer (30). We did not find any studies that examined the association between MBD and carbon monoxide. Since there is a positive correlation between MBD and breast cancer, the results of our study are consistent with the Taiwanese study.

It is very important to note that according to the evidence, environmental pollutants are associated with a higher risk of invasive breast cancer and increased mortality in these patients. Investigation on breast cancer cases has reported that PM_2.5 and NO₂ were associated with breast cancer (HR = 1.05; 95% CI: 0.99-1.11) and (HR = 1.06; 95%CI: 1.02-1.11), respectively. Invasive breast cancer was associated with PM_2.5 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) (31). The meta-analysis showed that each 10 µg/m³ of PM_2.5 was associated with a 1.17 (95% CI: 1.05-1.30) fold risk of BC mortality, and each 10 µg/m³ of PM₁₀ was associated with a 1.11 (95% CI: 1.02-1.21) fold risk of BC mortality. However, neither PM₁₀ nor PM_2.5 was found to be significantly associated with BC morbidity (32). These findings show the importance of research on the impact of pollutants on women's health, and public health professionals and policymakers should consider these characteristics to develop relevant interventions and prevention strategies that are more cost-effective and efficient.

The advantage of this study is, we considered all the possibly effective factors and known determinants of MBD, all of which are estrogen-related.

As it was demonstrated in Table 1, 91 (11.5%) and 106 (13.4%) women in the present study sample consumed metformin and aspirin, respectively. Numerous studies have evaluated the effects of aspirin and metformin on MBD with inconsistent results (33–36). In addition some researches have shown that higher vitamin D and calcium intake are associated with decreased MBD (37–39).

According to the findings of the mentioned studies and the high percentage of women who had taken metformin (11.5%), aspirin (13.4%), vitamin D (49.7%), and calcium (44.4%) in our study, it seems that without considering the use of these drugs, the results may not be expressed correctly. However, our findings showed that even by considering these factors, the results did not change.

It should be noted that most of the sampling in this study coincided with the worldwide onset of COVID-19 pandemic. Therefore, it is possible that the participants during this period were women at higher risk of breast cancer who had been referred for screening despite the COVID-19 pandemic.

In conclusion, based on our results, MBD was associated with higher levels of ambient air NO₂ and lower levels of CO. Considering our results and other evidences, important decisions at the national level are essential to reduce environmental pollution, especially air, to achieve sustainable development. Perhaps MBD monitoring as an available tool in each population, can help the prediction of future breast cancer occurrence and finding the high-risk geographic areas.

MBD: Mammographic breast density; IARC: International Agency for Research on Cancer; BPA: Bisphenol-A; PCBs: Polychlorinated biphenyl; NO₂: Nitrogen dioxide: SO₂: Sulfur dioxide: CO: Carbon monoxide; O_3: Ozone; PM: Particulate matter; OCP: Oral contraceptive; ACR: American College of Radiology; BI-RADS: Breast Imaging-Reporting and Data System; AQCC: Air Quality Control Company; WHO: World Health Organization; EPA: Environmental Protection Agency; IDW: Inverse distance weighting; SD: Standard deviation; OR: Odds ratio; CI: Confidence interval; BMI: Body mass index; HR: Hazard ratio; EDCs: Endocrine-disrupting chemicals; PAHs: Polycyclic aromatic hydrocarbons.

Ethics approval and consent to participate

The study was approved by the ethics committee of Tehran University of Medical Sciences (IR.TUMS.VCR.REC.1398.897), and all participants have signed an informed consent.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and analyzed during the current study are available on reasonable request from the corresponding author.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was funded by Institute for Environmental Research (IER) and by an academic research grant from the Deputy of Research of Tehran University of Medical Sciences (grant number: 98-3-259-45184)

Authors' contributions

BE contributed to the conception, design of the work, interpretation of data, and drafting of the manuscript. SA, RO, MSH contributed to the design of the work and substantively revised the manuscript. MMN contributed to the analysis and interpretation of data. KN, AA, MA, and LB contributed to the conception and acquisition of the data. MSH as a corresponding author contributed in all steps. All authors read and approved the final manuscript. They accepted their own contribution to this manuscript and accepted all responsibilities.

Acknowledgement

The authors are grateful to Tehran Air Quality Control Company (TAQCC) for providing ambient air pollutants concentrations data. Also, the authors would like to thank Zohre Samadi, Mina Mahmoodi, and Afsaneh Teimoori, for their assistance in collecting data, which made this project possible. We would like to express our special thanks to Faezeh Izadpanah who helped us in extracting environmental data.

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Air pollution Exposure And Mammographic Breast Density In Tehran, Iran

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Abstract

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Background

Methods

Study design and participants

Data Collection

Air pollution exposure assessment

Air quality data gathering

Air quality data processing

Individual long-term exposure assessment

Statistical analysis

Results

Discussion

Conclusion

List Of Abbreviations

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References

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