Mammographic density and exposure to air pollutants in premenopausal women: a cross-sectional study.
Background: Mammographic density (MD) is a well-established risk factor for breast cancer. Air pollution is a major public health concern and a recognized carcinogen. We aim to investigate the association between MD and exposure to specific air pollutants (SO, CO, NO, NO, NO, PM, PM, and O) in preme...
| Autores: | , , , , , , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Instituto de Salud Carlos III (ISCIII) |
| Repositorio: | Repisalud |
| Idioma: | inglés |
| OAI Identifier: | oai:repisalud.isciii.es:20.500.12105/25931 |
| Acceso en línea: | https://hdl.handle.net/20.500.12105/25931 |
| Access Level: | acceso abierto |
| Palabra clave: | Air pollution Breast cancer Breast density Correlation DDM-Madrid Kriging Long-term exposure Principal component analysis Adult Air Pollutants Air Pollution Breast Density Breast Neoplasms Cross-Sectional Studies Environmental Exposure Female Humans Mammography Middle Aged Premenopause Spain |
| Sumario: | Background: Mammographic density (MD) is a well-established risk factor for breast cancer. Air pollution is a major public health concern and a recognized carcinogen. We aim to investigate the association between MD and exposure to specific air pollutants (SO, CO, NO, NO, NO, PM, PM, and O) in premenopausal females. Methods:This cross-sectional study, carried out in Spain, included 769 participants who attended their gynecological examinations. Hourly concentrations of the pollutants were extracted from the Air Quality Monitoring System of Madrid City over a 3-year period. Individual long-term exposure to pollutants was assessed by geocoding residential addresses and monitoring stations, and applying ordinary kriging to the 3-year annual mean concentrations of each pollutant to interpolate the surface of Madrid. This exposure variable was categorized into quartiles. In a first analysis, we used multiple linear regression models with the log-transformed percent MD as a continuous variable. In a second analysis, we used MD as a dichotomous variable ("high" density (MD > 50%) vs. "low" density (MD ≤ 50%)) and applied multiple logistic regression models to estimate odds ratios (ORs). We also analyzed the correlation among the pollutants, and performed a principal component analysis (PCA) to reduce the dimensionality of this set of eight correlated pollutants into a smaller set of uncorrelated variables (principal components (PCs)). Finally, the initial analyses were applied to the PCs to detect underlying patterns of emission sources. Results: The first analysis detected no association between MD and exposure to any of the pollutants. The second analysis showed non-statistically significant increased risks (OR; IC95%) of high MD were detected in women with higher exposure to SO (1.50; 0.90-2.48), and PM (1.27; 0.77-2.10). In contrast, non-significant ORs < 1 were found in all exposure quartiles for NO (OR = 0.72, OR = 0.68, OR = 0.78), and PM (OR = 0.69, OR = 0.82, OR = 0.72). PCA identified two PCs (PC1: "traffic pollution" and PC2: "natural pollution"), and no association was detected between MD and proximity to these two PCs. Conclusions: In general, our results show a lack of association between residential exposure to specific air pollutants and MD in premenopausal females. Future research is needed to confirm or refute these findings. |
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