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Biomarkers of mammographic density in premenopausal women

BACKGROUND: While mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal wo...

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Autores principales: His, Mathilde, Lajous, Martin, Gómez-Flores-Ramos, Liliana, Monge, Adriana, Dossus, Laure, Viallon, Vivian, Gicquiau, Audrey, Biessy, Carine, Gunter, Marc J., Rinaldi, Sabina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305592/
https://www.ncbi.nlm.nih.gov/pubmed/34301304
http://dx.doi.org/10.1186/s13058-021-01454-3
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author His, Mathilde
Lajous, Martin
Gómez-Flores-Ramos, Liliana
Monge, Adriana
Dossus, Laure
Viallon, Vivian
Gicquiau, Audrey
Biessy, Carine
Gunter, Marc J.
Rinaldi, Sabina
author_facet His, Mathilde
Lajous, Martin
Gómez-Flores-Ramos, Liliana
Monge, Adriana
Dossus, Laure
Viallon, Vivian
Gicquiau, Audrey
Biessy, Carine
Gunter, Marc J.
Rinaldi, Sabina
author_sort His, Mathilde
collection PubMed
description BACKGROUND: While mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal women. Then, we aimed to identify potential correlates of these biomarkers to guide future research on potential modifiable determinants of mammographic density. METHODS: A total of 132 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, hexose) were measured by tandem liquid chromatography/mass spectrometry in plasma samples from 573 premenopausal participants in the Mexican Teachers’ Cohort. Associations between metabolites and percent mammographic density were assessed using linear regression models, adjusting for breast cancer risk factors and accounting for multiple tests. Mean concentrations of metabolites associated with percent mammographic density were estimated across levels of several lifestyle and metabolic factors. RESULTS: Sphingomyelin (SM) C16:1 and phosphatidylcholine (PC) ae C30:2 were inversely associated with percent mammographic density after correction for multiple tests. Linear trends with percent mammographic density were observed for SM C16:1 only in women with body mass index (BMI) below the median (27.4) and for PC ae C30:2 in women with a BMI over the median. SM C16:1 and PC ae C30:2 concentrations were positively associated with cholesterol (total and HDL) and inversely associated with number of metabolic syndrome components. CONCLUSIONS: We identified new biomarkers associated with mammographic density in young women. The association of these biomarkers with mammographic density and metabolic parameters may provide new perspectives to support future preventive actions for breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-021-01454-3.
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spelling pubmed-83055922021-07-28 Biomarkers of mammographic density in premenopausal women His, Mathilde Lajous, Martin Gómez-Flores-Ramos, Liliana Monge, Adriana Dossus, Laure Viallon, Vivian Gicquiau, Audrey Biessy, Carine Gunter, Marc J. Rinaldi, Sabina Breast Cancer Res Research Article BACKGROUND: While mammographic density is one of the strongest risk factors for breast cancer, little is known about its determinants, especially in young women. We applied targeted metabolomics to identify circulating metabolites specifically associated with mammographic density in premenopausal women. Then, we aimed to identify potential correlates of these biomarkers to guide future research on potential modifiable determinants of mammographic density. METHODS: A total of 132 metabolites (acylcarnitines, amino acids, biogenic amines, glycerophospholipids, sphingolipids, hexose) were measured by tandem liquid chromatography/mass spectrometry in plasma samples from 573 premenopausal participants in the Mexican Teachers’ Cohort. Associations between metabolites and percent mammographic density were assessed using linear regression models, adjusting for breast cancer risk factors and accounting for multiple tests. Mean concentrations of metabolites associated with percent mammographic density were estimated across levels of several lifestyle and metabolic factors. RESULTS: Sphingomyelin (SM) C16:1 and phosphatidylcholine (PC) ae C30:2 were inversely associated with percent mammographic density after correction for multiple tests. Linear trends with percent mammographic density were observed for SM C16:1 only in women with body mass index (BMI) below the median (27.4) and for PC ae C30:2 in women with a BMI over the median. SM C16:1 and PC ae C30:2 concentrations were positively associated with cholesterol (total and HDL) and inversely associated with number of metabolic syndrome components. CONCLUSIONS: We identified new biomarkers associated with mammographic density in young women. The association of these biomarkers with mammographic density and metabolic parameters may provide new perspectives to support future preventive actions for breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-021-01454-3. BioMed Central 2021-07-23 2021 /pmc/articles/PMC8305592/ /pubmed/34301304 http://dx.doi.org/10.1186/s13058-021-01454-3 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
His, Mathilde
Lajous, Martin
Gómez-Flores-Ramos, Liliana
Monge, Adriana
Dossus, Laure
Viallon, Vivian
Gicquiau, Audrey
Biessy, Carine
Gunter, Marc J.
Rinaldi, Sabina
Biomarkers of mammographic density in premenopausal women
title Biomarkers of mammographic density in premenopausal women
title_full Biomarkers of mammographic density in premenopausal women
title_fullStr Biomarkers of mammographic density in premenopausal women
title_full_unstemmed Biomarkers of mammographic density in premenopausal women
title_short Biomarkers of mammographic density in premenopausal women
title_sort biomarkers of mammographic density in premenopausal women
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8305592/
https://www.ncbi.nlm.nih.gov/pubmed/34301304
http://dx.doi.org/10.1186/s13058-021-01454-3
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