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Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program

Epidemiologic evidence for the association of cholesterol and breast cancer is inconsistent. Several factors may contribute to this inconsistency, including limited sample sizes, confounding effects of antihyperlipidemic treatment, age, and body mass index, and the assumption that the association fo...

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Autores principales: Feng, Jianglin, Symonds, Esteban Astiazaran, Karnes, Jason H
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841847/
https://www.ncbi.nlm.nih.gov/pubmed/36654923
http://dx.doi.org/10.1177/11769351221144132
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author Feng, Jianglin
Symonds, Esteban Astiazaran
Karnes, Jason H
author_facet Feng, Jianglin
Symonds, Esteban Astiazaran
Karnes, Jason H
author_sort Feng, Jianglin
collection PubMed
description Epidemiologic evidence for the association of cholesterol and breast cancer is inconsistent. Several factors may contribute to this inconsistency, including limited sample sizes, confounding effects of antihyperlipidemic treatment, age, and body mass index, and the assumption that the association follows a simple linear function. Here, we aimed to address these factors by combining visualization and quantification a large-scale contemporary electronic health record database (the All of Us Research Program). We find clear visual and quantitative evidence that breast cancer is strongly, positively, and near-linearly associated with total cholesterol and low-density lipoprotein cholesterol, but not associated with triglycerides. The association of breast cancer with high-density lipoprotein cholesterol was non-linear and age dependent. Standardized odds ratios were 2.12 (95% confidence interval 1.9-2.48), P = 5.6 × 10(−31) for total cholesterol; 1.99 (1.75-2.26), P = 2.6 × 10(−26) for low-density lipoprotein cholesterol; 1.69 (1.3-2.2), P = 9.0 × 10(−5) for high-density lipoprotein cholesterol at age < 56; and 0.65 (0.55-0.78), P = 1.2 × 10(−6) for high-density lipoprotein cholesterol at age ⩾ 56. The inclusion of the lipid levels measured after antihyperlipidemic treatment in the analysis results in erroneous associations. We demonstrate that the use of the logistic regression without inspecting risk variable linearity and accounting for confounding effects may lead to inconsistent results.
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spelling pubmed-98418472023-01-17 Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program Feng, Jianglin Symonds, Esteban Astiazaran Karnes, Jason H Cancer Inform Original Research Epidemiologic evidence for the association of cholesterol and breast cancer is inconsistent. Several factors may contribute to this inconsistency, including limited sample sizes, confounding effects of antihyperlipidemic treatment, age, and body mass index, and the assumption that the association follows a simple linear function. Here, we aimed to address these factors by combining visualization and quantification a large-scale contemporary electronic health record database (the All of Us Research Program). We find clear visual and quantitative evidence that breast cancer is strongly, positively, and near-linearly associated with total cholesterol and low-density lipoprotein cholesterol, but not associated with triglycerides. The association of breast cancer with high-density lipoprotein cholesterol was non-linear and age dependent. Standardized odds ratios were 2.12 (95% confidence interval 1.9-2.48), P = 5.6 × 10(−31) for total cholesterol; 1.99 (1.75-2.26), P = 2.6 × 10(−26) for low-density lipoprotein cholesterol; 1.69 (1.3-2.2), P = 9.0 × 10(−5) for high-density lipoprotein cholesterol at age < 56; and 0.65 (0.55-0.78), P = 1.2 × 10(−6) for high-density lipoprotein cholesterol at age ⩾ 56. The inclusion of the lipid levels measured after antihyperlipidemic treatment in the analysis results in erroneous associations. We demonstrate that the use of the logistic regression without inspecting risk variable linearity and accounting for confounding effects may lead to inconsistent results. SAGE Publications 2023-01-12 /pmc/articles/PMC9841847/ /pubmed/36654923 http://dx.doi.org/10.1177/11769351221144132 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Feng, Jianglin
Symonds, Esteban Astiazaran
Karnes, Jason H
Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program
title Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program
title_full Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program
title_fullStr Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program
title_full_unstemmed Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program
title_short Visualization and Quantification of the Association Between Breast Cancer and Cholesterol in the All of Us Research Program
title_sort visualization and quantification of the association between breast cancer and cholesterol in the all of us research program
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9841847/
https://www.ncbi.nlm.nih.gov/pubmed/36654923
http://dx.doi.org/10.1177/11769351221144132
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