2026-04-29T22:57:42Zhttps://repisalud.isciii.es/rest/oai/requestoai:repisalud.isciii.es:20.500.12105/167892025-05-05T11:16:55Zcom_20.500.12105_2052com_20.500.12105_2051com_20.500.12105_15322col_20.500.12105_19608col_20.500.12105_16938
Repisalud
author
Jiménez, Tamara
author
Pollan-Santamaria, Marina
author
Domínguez-Castillo, Alejandro
author
de Lucas, Maria Pilar
author
Sierra, Maria Angeles
author
Castelló Pastor, Adela
author
Fernandez de Larrea-Baz, Nerea
author
Lora-Pablos, David
author
Salas-Trejo, Dolores
author
Llobet, Rafael
author
Martínez, Inmaculada
author
Pino, Marina Nieves
author
Martínez-Cortés, Mercedes
author
Perez-Gomez, Beatriz
author
Lope Carvajal, Virginia
author
García-Pérez, Javier
funder
Instituto de Salud Carlos III
2023-12-13T09:23:25Z
2023-12-13T09:23:25Z
2023-06-10
Sci Total Environ. 2023 Jun 10:876:162768.
10.1016/j.scitotenv.2023.162768
1879-1026
The Science of the total environment
http://hdl.handle.net/20.500.12105/16789
Background: Mammographic density (MD), defined as the percentage of dense fibroglandular tissue in the breast, is a modifiable marker of the risk of developing breast cancer. Our objective was to evaluate the effect of residential proximity to an increasing number of industrial sources in MD. Methods: A cross-sectional study was conducted on 1225 premenopausal women participating in the DDM-Madrid study. We calculated distances between women's houses and industries. The association between MD and proximity to an increasing number of industrial facilities and industrial clusters was explored using multiple linear regression models. Results: We found a positive linear trend between MD and proximity to an increasing number of industrial sources for all industries, at distances of 1.5 km (p-trend = 0.055) and 2 km (p-trend = 0.083). Moreover, 62 specific industrial clusters were analyzed, highlighting the significant associations found between MD and proximity to the following 6 industrial clusters: cluster 10 and women living at ≤1.5 km (β = 10.78, 95 % confidence interval (95%CI) = 1.59; 19.97) and at ≤2 km (β = 7.96, 95%CI = 0.21; 15.70); cluster 18 and women residing at ≤3 km (β = 8.48, 95%CI = 0.01; 16.96); cluster 19 and women living at ≤3 km (β = 15.72, 95%CI = 1.96; 29.49); cluster 20 and women living at ≤3 km (β = 16.95, 95%CI = 2.90; 31.00); cluster 48 and women residing at ≤3 km (β = 15.86, 95%CI = 3.95; 27.77); and cluster 52 and women living at ≤2.5 km (β = 11.09, 95%CI = 0.12; 22.05). These clusters include the following industrial activities: surface treatment of metals/plastic, surface treatment using organic solvents, production/processing of metals, recycling of animal waste, hazardous waste, urban waste-water treatment plants, inorganic chemical industry, cement and lime, galvanization, and food/beverage sector. Conclusions: Our results suggest that women living in the proximity to an increasing number of industrial sources and those near certain types of industrial clusters have higher MD.
eng
Breast cancer
Breast density
Industrial cluster
Industrial pollution
Industry
Mammographic density in the environs of multiple industrial sources
research article
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Supplementary_MammographicDensityEnvironsMultiple_2023.pdf
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MammographicDensityEnvironsMultiple_2023.pdf.txt
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Supplementary_MammographicDensityEnvironsMultiple_2023.pdf.txt