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Jiménez T, Domínguez-Castillo A, Fernández de Larrea-Baz N, Lucas P, Sierra MÁ, Salas-Trejo D, Llobet R, Martínez I, Pino MN, Martínez-Cortés M, Pérez-Gómez B, Pollán M, Lope V, García-Pérez J. Residential exposure to traffic pollution and mammographic density in premenopausal women. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 928:172463. [PMID: 38615764 DOI: 10.1016/j.scitotenv.2024.172463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 04/11/2024] [Accepted: 04/11/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Mammographic density (MD) is the most important breast cancer biomarker. Ambient pollution is a carcinogen, and its relationship with MD is unclear. This study aims to explore the association between exposure to traffic pollution and MD in premenopausal women. METHODOLOGY This Spanish cross-sectional study involved 769 women attending gynecological examinations in Madrid. Annual Average Daily Traffic (AADT), extracted from 1944 measurement road points provided by the City Council of Madrid, was weighted by distances (d) between road points and women's addresses to develop a Weighted Traffic Exposure Index (WTEI). Three methods were employed: method-1 (1dAADT), method-2 (1dAADT), and method-3 (e1dAADT). Multiple linear regression models, considering both log-transformed percentage of MD and untransformed MD, were used to estimate MD differences by WTEI quartiles, through two strategies: "exposed (exposure buffers between 50 and 200 m) vs. not exposed (>200 m)"; and "degree of traffic exposure". RESULTS Results showed no association between MD and traffic pollution according to buffers of exposure to the WTEI (first strategy) for the three methods. The highest reductions in MD, although not statistically significant, were detected in the quartile with the highest traffic exposure. For instance, method-3 revealed a suggestive inverse trend (eβQ1 = 1.23, eβQ2 = 0.96, eβQ3 = 0.85, eβQ4 = 0.85, p-trend = 0.099) in the case of 75 m buffer. Similar non-statistically significant trends were observed with Methods-1 and -2. When we examined the effect of traffic exposure considering all the 1944 measurement road points in every participant (second strategy), results showed no association for any of the three methods. A slightly decreased MD, although not significant, was observed only in the quartile with the highest traffic exposure: eβQ4 = 0.98 (method-1), and eβQ4 = 0.95 (methods-2 and -3). CONCLUSIONS Our results showed no association between exposure to traffic pollution and MD in premenopausal women. Further research is needed to validate these findings.
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Affiliation(s)
- Tamara Jiménez
- Department of Preventive Medicine, Public Health and Microbiology, Universidad Autónoma de Madrid (UAM), Madrid, Spain; HM CINAC (Centro Integral de Neurociencias AC), Hospital Universitario Puerta del Sur, Fundación HM Hospitales, Móstoles, Spain
| | - Alejandro Domínguez-Castillo
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain.
| | - Nerea Fernández de Larrea-Baz
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Pilar Lucas
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain.
| | - María Ángeles Sierra
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Dolores Salas-Trejo
- Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain; Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Rafael Llobet
- Institute of Computer Technology, Universitat Politècnica de València, Valencia, Spain.
| | - Inmaculada Martínez
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Marina Nieves Pino
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Mercedes Martínez-Cortés
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Beatriz Pérez-Gómez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Marina Pollán
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Virginia Lope
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Javier García-Pérez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
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Jiménez T, Pollán M, Domínguez-Castillo A, Lucas P, Sierra MÁ, Castelló A, Fernández de Larrea-Baz N, Lora-Pablos D, Salas-Trejo D, Llobet R, Martínez I, Pino MN, Martínez-Cortés M, Pérez-Gómez B, Lope V, García-Pérez J. Mammographic density in the environs of multiple industrial sources. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 876:162768. [PMID: 36907418 DOI: 10.1016/j.scitotenv.2023.162768] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
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.
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Affiliation(s)
- Tamara Jiménez
- Department of Preventive Medicine, Public Health and Microbiology, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Marina Pollán
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Alejandro Domínguez-Castillo
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain.
| | - Pilar Lucas
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain.
| | - María Ángeles Sierra
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Adela Castelló
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Nerea Fernández de Larrea-Baz
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - David Lora-Pablos
- Scientific Support Unit, Instituto de Investigación Sanitaria Hospital Universitario 12 de Octubre (imas12), Madrid, Spain; Spanish Clinical Research Network (SCReN), Madrid, Spain; Faculty of Statistical Studies, Universidad Complutense de Madrid (UCM), Madrid, Spain.
| | - Dolores Salas-Trejo
- Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain; Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Rafael Llobet
- Institute of Computer Technology, Universitat Politècnica de València, Valencia, Spain.
| | - Inmaculada Martínez
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Marina Nieves Pino
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Mercedes Martínez-Cortés
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Beatriz Pérez-Gómez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Virgina Lope
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Javier García-Pérez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health (Instituto de Salud Carlos III), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
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Jiménez T, Pollán M, Domínguez-Castillo A, Lucas P, Sierra MÁ, Fernández de Larrea-Baz N, González-Sánchez M, Salas-Trejo D, Llobet R, Martínez I, Pino MN, Martínez-Cortés M, Pérez-Gómez B, Lope V, García-Pérez J. Residential proximity to industrial pollution and mammographic density. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 829:154578. [PMID: 35304152 DOI: 10.1016/j.scitotenv.2022.154578] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 02/25/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Mammographic density (MD), expressed as percentage of fibroglandular breast tissue, is an important risk factor for breast cancer. Our objective is to investigate the relationship between MD and residential proximity to pollutant industries in premenopausal Spanish women. METHODS A cross-sectional study was carried out in a sample of 1225 women extracted from the DDM-Madrid study. Multiple linear regression models were used to assess the association of MD percentage (and their 95% confidence intervals (95%CIs)) and proximity (between 1 km and 3 km) to industries included in the European Pollutant Release and Transfer Register. RESULTS Although no association was found between MD and distance to all industries as a whole, several industrial sectors showed significant association for some distances: "surface treatment of metals and plastic" (β = 4.98, 95%CI = (0.85; 9.12) at ≤1.5 km, and β = 3.00, 95%CI = (0.26; 5.73) at ≤2.5 km), "organic chemical industry" (β = 6.73, 95%CI = (0.50; 12.97) at ≤1.5 km), "pharmaceutical products" (β = 4.14, 95%CI = (0.58; 7.70) at ≤2 km; β = 3.55, 95%CI = (0.49; 6.60) at ≤2.5 km; and β = 3.11, 95%CI = (0.20; 6.01) at ≤3 km), and "urban waste-water treatment plants" (β = 8.06, 95%CI = (0.82; 15.30) at ≤1 km; β = 5.28; 95%CI = (0.49; 10.06) at ≤1.5 km; β = 4.30, 95%CI = (0.03; 8.57) at ≤2 km; β = 5.26, 95%CI = (1.83; 8.68) at ≤2.5 km; and β = 3.19, 95%CI = (0.46; 5.92) at ≤3 km). Moreover, significant increased MD was observed in women close to industries releasing specific pollutants: ammonia (β = 4.55, 95%CI = (0.26; 8.83) at ≤1.5 km; and β = 3.81, 95%CI = (0.49; 7.14) at ≤2 km), dichloromethane (β = 3.86, 95%CI = (0.00; 7.71) at ≤2 km), ethylbenzene (β = 8.96, 95%CI = (0.57; 17.35) at ≤3 km), and phenols (β = 2.60, 95%CI = (0.21; 5.00) at ≤2.5 km). CONCLUSIONS Our results suggest no statistically significant relationship between MD and proximity to industries as a whole, although we detected associations with various industrial sectors and some specific pollutants, which suggests that MD could have a mediating role in breast carcinogenesis.
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Affiliation(s)
- Tamara Jiménez
- Department of Preventive Medicine, Public Health and Microbiology, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Marina Pollán
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Alejandro Domínguez-Castillo
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain.
| | - Pilar Lucas
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain.
| | - María Ángeles Sierra
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Nerea Fernández de Larrea-Baz
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Mario González-Sánchez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Dolores Salas-Trejo
- Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain; Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Rafael Llobet
- Institute of Computer Technology, Universitat Politècnica de València, Valencia, Spain.
| | - Inmaculada Martínez
- Valencian Breast Cancer Screening Program, General Directorate of Public Health, Valencia, Spain; Center for Public Health Research CSISP, FISABIO, Valencia, Spain.
| | - Marina Nieves Pino
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Mercedes Martínez-Cortés
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, Madrid, Spain.
| | - Beatriz Pérez-Gómez
- Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain; Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain.
| | - Virginia Lope
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
| | - Javier García-Pérez
- Cancer and Environmental Epidemiology Unit, Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Instituto de Salud Carlos III (Carlos III Institute of Health), Madrid, Spain; Consortium for Biomedical Research in Epidemiology & Public Health (CIBER en Epidemiología y Salud Pública - CIBERESP), Spain.
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Toribio MJ, Priego-Capote F, Pérez-Gómez B, Fernández de Larrea-Baz N, Ruiz-Moreno E, Castelló A, Lucas P, Sierra MÁ, Pino MN, Martínez-Cortés M, Luque de Castro MD, Lope V, Pollán M. Factors Associated with Serum Vitamin D Metabolites and Vitamin D Metabolite Ratios in Premenopausal Women. Nutrients 2021; 13:3747. [PMID: 34836003 PMCID: PMC8621214 DOI: 10.3390/nu13113747] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2021] [Revised: 10/21/2021] [Accepted: 10/22/2021] [Indexed: 11/16/2022] Open
Abstract
The most representative indicator of vitamin D status in clinical practice is 25(OH)D3, but new biomarkers could improve the assessment of vitamin D status and metabolism. The objective of this study is to investigate the association of serum vitamin D metabolites and vitamin D metabolite ratios (VMRs) with potentially influential factors in premenopausal women. This is a cross-sectional study based on 1422 women, aged 39-50, recruited from a Madrid Medical Diagnostic Center. Participants answered an epidemiological and a food frequency questionnaire. Serum vitamin D metabolites were determined using an SPE-LC-MS/MS platform. The association between participant's characteristics, vitamin D metabolites, and VMRs was quantified by multiple linear regression models. Mean 25(OH)D3 concentration was 49.2 + 18.9 nmol/L, with greater deficits among obese, nulliparous, dark-skinned women, and with less sun exposure. A lower R2 ratio (1,25(OH)2D3/25(OH)D3) and a higher R4 (24,25(OH)2D3/1,25(OH)2D3) were observed in nulliparous women, with high sun exposure, and those with low caloric intake or high consumption of calcium, vitamin D supplements, or alcohol. Nulliparous women had lower R1 (25(OH)D3/Vit D3) and R3 (24,25(OH)2D3/25(OH)D3), and older women showed lower R3 and R4. Vitamin D status modified the association of the VMRs with seasons. VMRs can be complementary indicators of vitamin D status and its endogenous metabolism, and reveal the influence of certain individual characteristics on the expression of hydroxylase enzymes.
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Affiliation(s)
- María José Toribio
- Servicio de Admisión, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain;
- Department of Preventive Medicine, Public Health and Microbiology, Universidad Autónoma de Madrid, 28029 Madrid, Spain
| | - Feliciano Priego-Capote
- Department of Analytical Chemistry, University of Córdoba, 14014 Córdoba, Spain; (F.P.-C.); (M.D.L.d.C.)
- Maimónides Institute of Biomedical Research (IMIBIC), Reina Sofia University Hospital, University of Córdoba, 14004 Córdoba, Spain
| | - Beatriz Pérez-Gómez
- Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain; (B.P.-G.); (N.F.d.L.-B.); (E.R.-M.); (P.L.); (M.Á.S.); (M.P.)
- Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, 28029 Madrid, Spain
| | - Nerea Fernández de Larrea-Baz
- Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain; (B.P.-G.); (N.F.d.L.-B.); (E.R.-M.); (P.L.); (M.Á.S.); (M.P.)
- Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, 28029 Madrid, Spain
| | - Emma Ruiz-Moreno
- Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain; (B.P.-G.); (N.F.d.L.-B.); (E.R.-M.); (P.L.); (M.Á.S.); (M.P.)
- Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, 28029 Madrid, Spain
| | - Adela Castelló
- Faculty of Medicine, University of Alcalá, 28871 Alcalá de Henares, Spain;
| | - Pilar Lucas
- Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain; (B.P.-G.); (N.F.d.L.-B.); (E.R.-M.); (P.L.); (M.Á.S.); (M.P.)
| | - María Ángeles Sierra
- Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain; (B.P.-G.); (N.F.d.L.-B.); (E.R.-M.); (P.L.); (M.Á.S.); (M.P.)
- Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, 28029 Madrid, Spain
| | - Marina Nieves Pino
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, 28007 Madrid, Spain; (M.N.P.); (M.M.-C.)
| | - Mercedes Martínez-Cortés
- Servicio de Prevención y Promoción de la Salud, Madrid Salud, Ayuntamiento de Madrid, 28007 Madrid, Spain; (M.N.P.); (M.M.-C.)
| | - María Dolores Luque de Castro
- Department of Analytical Chemistry, University of Córdoba, 14014 Córdoba, Spain; (F.P.-C.); (M.D.L.d.C.)
- Maimónides Institute of Biomedical Research (IMIBIC), Reina Sofia University Hospital, University of Córdoba, 14004 Córdoba, Spain
| | - Virginia Lope
- Servicio de Admisión, Hospital General Universitario Gregorio Marañón, 28007 Madrid, Spain;
- Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain; (B.P.-G.); (N.F.d.L.-B.); (E.R.-M.); (P.L.); (M.Á.S.); (M.P.)
- Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, 28029 Madrid, Spain
| | - Marina Pollán
- Department of Epidemiology of Chronic Diseases, National Center for Epidemiology, Carlos III Institute of Health, 28029 Madrid, Spain; (B.P.-G.); (N.F.d.L.-B.); (E.R.-M.); (P.L.); (M.Á.S.); (M.P.)
- Consortium for Biomedical Research in Epidemiology & Public Health, CIBERESP, 28029 Madrid, Spain
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