Systemic autoimmune rheumatic diseases (SARDs) are chronic inflammatory and immuno-modulatory conditions that have been suggested to affect cancer risk. Using the Surveillance, Epidemiology and End Results-Medicare-linked database, women aged 67-99 years and diagnosed with incident breast cancer in 1993-2002 (n=84 778) were compared with an equal number of age-matched cancer-free female controls. Diagnoses of SARDs, including rheumatoid arthritis (RA, n=5238), systemic lupus erythematosus (SLE, n=340), Sjogren's syndrome (n=374), systemic sclerosis (n=128), and dermatomyositis (n=31), were determined from claim files for individuals from age 65 years to 1 year before selection. Associations of SARD diagnoses with breast cancer, overall and by oestrogen receptor (ER) expression, were assessed using odds ratio (OR) estimates from multivariable logistic regression models. The women diagnosed with RA were less likely to develop breast cancer (OR=0.87, 95% confidence interval (CI)=0.82-0.93). The risk reduction did not differ by tumour ER-status (OR=0.83, 95% CI=0.78-0.89 for ER-positive vs OR=0.91, 95% CI=0.81-1.04 for ER-negative, P for heterogeneity=0.14). The breast cancer risk was not associated with any of the other SARDs, except for a risk reduction of ER-negative cases (OR=0.49, 95% CI=0.26-0.93) among women with SLE. These findings suggest that systemic inflammation may affect breast epithelial neoplasia.
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http://dx.doi.org/10.1038/sj.bjc.6604906 | DOI Listing |
Breast Cancer Res
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Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
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View Article and Find Full Text PDFBMC Cancer
January 2025
Faculty of Medicine, University of Cologne and Institute for Health Economics and Clinical Epidemiology, University Hospital Cologne, Cologne, Germany.
Background: Patients who actively engage in their medical decision-making processes can experience better health outcomes. This exploratory study aimed to identify predictors of preferred and actual roles in decision-making in healthy women with BRCA1/2 pathogenic variants (PVs).
Methods: Women with BRCA1/2 PVs without a history of breast and/or ovarian cancer were recruited in six centres across Germany.
Radiol Med
January 2025
Department of Translational Medicine, University of Ferrara, Ferrara, Italy.
Purpose: Build machine learning (ML) models able to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer (BC) patients based on conventional and radiomic signatures extracted from baseline [F]FDG PET/CT.
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Ann Surg Oncol
January 2025
Department of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Curr Oncol Rep
January 2025
Melbourne School of Health Sciences, The University of Melbourne, The Royal Women's Hospital, Melbourne; Mercy Hospital for Women, Melbourne, VIC, Australia.
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