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      Mammographic breast density and its association with urinary estrogens and the fecal microbiota in postmenopausal women

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          Abstract

          Background

          Breast density, as estimated by mammography, is a strong risk factor for breast cancer in pre- and postmenopausal women, but the determinants of breast density have not yet been established. The aim of this study was to assess if urinary estrogens or gut microbiota alterations are associated with mammographic density in postmenopausal women.

          Methods

          Among 54 cancer-free, postmenopausal controls in the Breast and Colon Health study, we classified low- versus high-density women with Breast Imaging Reporting and Data System (BI-RADS, 5 th edition) mammographic screening data, then assessed associations with urinary estrogens and estrogen metabolites (determined by liquid chromatography/tandem mass spectrometry), and fecal microbiota alpha and beta diversity (using Illumina sequencing of 16S rRNA amplicons).

          Results

          Multiple logistic regression revealed no significant association between breast density and fecal microbiota metrics (PD_tree P-value = 0.82; un-weighted and weighted UniFrac P = 0.92 and 0.83, respectively, both by MiRKAT). In contrast, total urinary estrogens (and all 15 estrogens/estrogen metabolites) were strongly and inversely associated with breast density (P = 0.01) after adjustment for age and body mass index.

          Conclusion

          Mammographic density was not associated with the gut microbiota, but it was inversely associated with urinary estrogen levels.

          Impact

          The finding of an inverse association between urinary estrogens and breast density in cancer-free women adds to the growing breast cancer literature on understanding the relationship between endogenous estrogens and mammographic density.

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          Most cited references45

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          The Intestinal Microbiome and Estrogen Receptor-Positive Female Breast Cancer.

          The huge communities of residential microbes, including bacteria, viruses, Archaea, and Eukaryotes, that colonize humans are increasingly recognized as playing important roles in health and disease. A complex populous ecosystem, the human gastrointestinal (GI) tract harbors up to 10(11) bacterial cells per gram of luminal content, whose collective genome, the gut metagenome, contains a vastly greater number of individual genes than the human genome. In health, the function of the microbiome might be considered to be in dynamic equilibrium with the host, exerting both local and distant effects. However, 'disequilibrium' may contribute to the emergence of disease, including malignancy. In this review, we discuss how the intestinal bacterial microbiome and in particular how an 'estrobolome,' the aggregate of enteric bacterial genes capable of metabolizing estrogens, might affect women's risk of developing postmenopausal estrogen receptor-positive breast cancer. Estrobolome composition is impacted by factors that modulate its functional activity. Exploring variations in the composition and activities of the estrobolome in healthy individuals and in women with estrogen-driven breast cancer may lead to development of microbiome-based biomarkers and future targeted interventions to attenuate cancer risk.
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            Endogenous estrogen, androgen, and progesterone concentrations and breast cancer risk among postmenopausal women.

            Levels of endogenous hormones have been associated with the risk of breast cancer among postmenopausal women. Little research, however, has investigated the association between hormone levels and tumor receptor status or invasive versus in situ tumor status. Nor has the relation between breast cancer risk and postmenopausal progesterone levels been investigated. We prospectively investigated these relations in a case-control study nested within the Nurses' Health Study. Blood samples were prospectively collected during 1989 and 1990. Among eligible postmenopausal women, 322 cases of breast cancer (264 invasive, 41 in situ, 153 estrogen receptor [ER]-positive and progesterone receptor [PR]-positive [ER+/PR+], and 39 ER-negative and PR-negative [ER-/PR-] disease) were reported through June 30, 1998. For each case subject, two control subjects (n = 643) were matched on age and blood collection (by month and time of day). Endogenous hormone levels were measured in blood plasma. We used conditional and unconditional logistic regression analyses to assess associations and to control for established breast cancer risk factors. We observed a statistically significant direct association between breast cancer risk and the level of both estrogens and androgens, but we did not find any (by year) statistically significant associations between this risk and the level of progesterone or sex hormone binding globulin. When we restricted the analysis to case subjects with ER+/PR+ tumors and compared the highest with the lowest fourths of plasma hormone concentration, we observed an increased risk of breast cancer associated with estradiol (relative risk [RR] = 3.3, 95% confidence interval [CI] = 2.0 to 5.4), testosterone (RR = 2.0, 95% CI = 1.2 to 3.4), androstenedione (RR = 2.5, 95% CI = 1.4 to 4.3), and dehydroepiandrosterone sulfate (RR = 2.3, 95% CI = 1.3 to 4.1). In addition, all hormones tended to be associated most strongly with in situ disease. Circulating levels of sex steroid hormones may be most strongly associated with risk of ER+/PR+ breast tumors.
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              Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test.

              High-throughput sequencing technology has enabled population-based studies of the role of the human microbiome in disease etiology and exposure response. Distance-based analysis is a popular strategy for evaluating the overall association between microbiome diversity and outcome, wherein the phylogenetic distance between individuals' microbiome profiles is computed and tested for association via permutation. Despite their practical popularity, distance-based approaches suffer from important challenges, especially in selecting the best distance and extending the methods to alternative outcomes, such as survival outcomes. We propose the microbiome regression-based kernel association test (MiRKAT), which directly regresses the outcome on the microbiome profiles via the semi-parametric kernel machine regression framework. MiRKAT allows for easy covariate adjustment and extension to alternative outcomes while non-parametrically modeling the microbiome through a kernel that incorporates phylogenetic distance. It uses a variance-component score statistic to test for the association with analytical p value calculation. The model also allows simultaneous examination of multiple distances, alleviating the problem of choosing the best distance. Our simulations demonstrated that MiRKAT provides correctly controlled type I error and adequate power in detecting overall association. "Optimal" MiRKAT, which considers multiple candidate distances, is robust in that it suffers from little power loss in comparison to when the best distance is used and can achieve tremendous power gain in comparison to when a poor distance is chosen. Finally, we applied MiRKAT to real microbiome datasets to show that microbial communities are associated with smoking and with fecal protease levels after confounders are controlled for.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Project administrationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Project administrationRole: Writing – review & editing
                Role: Formal analysisRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: VisualizationRole: Writing – review & editing
                Role: Data curation
                Role: Data curationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Formal analysisRole: Writing – review & editing
                Role: Data curationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                8 May 2019
                2019
                : 14
                : 5
                : e0216114
                Affiliations
                [1 ] Department of Epidemiology, University of North Carolina Chapel Hill, Chapel Hill, North Carolina, United States of America
                [2 ] Institute for Health Research, Kaiser Permanente Colorado, Denver, Colorado, United States of America
                [3 ] Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland, United States of America
                [4 ] Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
                [5 ] Division of Cancer Prevention, National Cancer Institute, Bethesda, Maryland, United States of America
                [6 ] Cancer Research Technology Program, Leidos Biomedical Research Incorporated, Frederick National Laboratory for Cancer Research, Frederick, Maryland, United States of America
                Medical University of South Carolina, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0001-9537-6470
                http://orcid.org/0000-0001-5398-761X
                http://orcid.org/0000-0002-9856-8010
                Article
                PONE-D-18-15855
                10.1371/journal.pone.0216114
                6505928
                31067262
                2aae08db-870f-446a-a080-b7e68ced23e9

                This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

                History
                : 26 May 2018
                : 15 April 2019
                Page count
                Figures: 4, Tables: 3, Pages: 15
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100008615, Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill;
                Award ID: T32CA057726
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100011541, Division of Cancer Epidemiology and Genetics, National Cancer Institute;
                Award ID: Z01CP010214
                Award Recipient :
                This work was supported by the Intramural Research Program of the National Cancer Institute at the National Institutes of Health (Z01CP010214). G.J. was supported by the UNC Lineberger Cancer Control Education Program (T32CA057726).
                Categories
                Research Article
                Biology and Life Sciences
                Biochemistry
                Hormones
                Estrogens
                Biology and Life Sciences
                Microbiology
                Medical Microbiology
                Microbiome
                Biology and Life Sciences
                Genetics
                Genomics
                Microbial Genomics
                Microbiome
                Biology and Life Sciences
                Microbiology
                Microbial Genomics
                Microbiome
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Breast Tumors
                Breast Cancer
                Biology and Life Sciences
                Biochemistry
                Metabolism
                Metabolites
                Biology and Life Sciences
                Organisms
                Bacteria
                Gut Bacteria
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Body Mass Index
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Body Mass Index
                Biology and Life Sciences
                Biochemistry
                Hormones
                Lipid Hormones
                Estradiol
                Medicine and Health Sciences
                Public and Occupational Health
                Physical Activity
                Custom metadata
                All data is available in the National Center for Biotechnology Information (NCBI) - Sequence Read Archive: https://trace.ncbi.nlm.nih.gov/Traces/study/?acc=SRP091508&go=go.

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                Uncategorized

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