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Sökning: WFRF:(Eliassen A Heather) > (2019)

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1.
  • Lucht, Sarah A., et al. (författare)
  • Circulating lipids, mammographic density, and risk of breast cancer in the Nurses’ Health Study and Nurses’ Health Study II
  • 2019
  • Ingår i: Cancer Causes and Control. - Springer. - 0957-5243. ; 30:9, s. 943-953
  • Tidskriftsartikel (refereegranskat)abstract
    • Purpose: Epidemiologic evidence supports an association between high mammographic density and increased breast cancer risk yet etiologic mechanisms remain largely unknown. Mixed evidence exists as to whether circulating lipid levels influence mammographic density and breast cancer risk. Therefore, we examined these associations in the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII), two large prospective cohorts with information on PMD and circulating lipid measures, long follow-up, and breast cancer risk factor and outcome data. Methods: We conducted a nested case–control study among women in the NHS and NHSII. Percent mammographic density (PMD) was measured using Cumulus software, a computer-assisted method, on digitized film mammograms. Cross-sectional associations between circulating lipids [total cholesterol (n = 1,502), high-density lipoprotein (HDL-C; n = 579), and triglycerides (n = 655)] and PMD were evaluated among controls. All analyses were stratified by menopausal status at time of mammogram. Relative risks for breast cancer by lipid and PMD measures were estimated among postmenopausal women in the full nested case–control study (cases/controls for cholesterol, HDL-C, and triglycerides were 937/975, 416/449, and 506/537, respectively). Results: There were no significant associations between circulating lipid levels and PMD among healthy women, irrespective of menopausal status. The association between PMD and breast cancer risk among postmenopausal women was not modified by circulating lipid levels (p interaction = 0.83, 0.80, and 0.34 for total cholesterol, HDL-C, and triglycerides, respectively). Conclusion: Overall, no association was observed between lipid levels and PMD, and there was no evidence that lipid levels modified the association between PMD and breast cancer risk.
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2.
  • Nichols, Hazel B, et al. (författare)
  • Breast Cancer Risk After Recent Childbirth : A Pooled Analysis of 15 Prospective Studies
  • 2019
  • Ingår i: Annals of Internal Medicine. - 0003-4819 .- 1539-3704. ; 170:1, s. 22-30
  • Tidskriftsartikel (refereegranskat)abstract
    • <p><strong>Background:</strong> Parity is widely recognized as protective for breast cancer, but breast cancer risk may be increased shortly after childbirth. Whether this risk varies with breastfeeding, family history of breast cancer, or specific tumor subtype has rarely been evaluated.</p><p><strong>Objective:</strong> To characterize breast cancer risk in relation to recent childbirth.</p><p><strong>Design:</strong> Pooled analysis of individual-level data from 15 prospective cohort studies.</p><p><strong>Setting:</strong> The international Premenopausal Breast Cancer Collaborative Group.</p><p><strong>Participants:</strong> Women younger than 55 years.</p><p><strong>Measurements:</strong> During 9.6 million person-years of follow-up, 18 826 incident cases of breast cancer were diagnosed. Hazard ratios (HRs) and 95% CIs for breast cancer were calculated using Cox proportional hazards regression.</p><p><strong>Results:</strong> Compared with nulliparous women, parous women had an HR for breast cancer that peaked about 5 years after birth (HR, 1.80 [95% CI, 1.63 to 1.99]) before decreasing to 0.77 (CI, 0.67 to 0.88) after 34 years. The association crossed over from positive to negative about 24 years after birth. The overall pattern was driven by estrogen receptor (ER)-positive breast cancer; no crossover was seen for ER-negative cancer. Increases in breast cancer risk after childbirth were pronounced when combined with a family history of breast cancer and were greater for women who were older at first birth or who had more births. Breastfeeding did not modify overall risk patterns.</p><p><strong>Limitations:</strong> Breast cancer diagnoses during pregnancy were not uniformly distinguishable from early postpartum diagnoses. Data on human epidermal growth factor receptor 2 (HER2) oncogene overexpression were limited.</p><p><strong>Conclusion:</strong> Compared with nulliparous women, parous women have an increased risk for breast cancer for more than 20 years after childbirth. Health care providers should consider recent childbirth a risk factor for breast cancer in young women.</p><p><strong>Primary Funding Source:</strong> The Avon Foundation, the National Institute of Environmental Health Sciences, Breast Cancer Now and the UK National Health Service, and the Institute of Cancer Research.</p>
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3.
  • Clendenen, Tess V., et al. (författare)
  • Breast cancer risk prediction in women aged 35-50 years impact of including sex hormone concentrations in the Gail model
  • 2019
  • Ingår i: Breast Cancer Research. - BioMed Central. - 1465-5411 .- 1465-542X. ; 21
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>Background: Models that accurately predict risk of breast cancer are needed to help younger women make decisions about when to begin screening. Premenopausal concentrations of circulating anti-Mullerian hormone (AMH), a biomarker of ovarian reserve, and testosterone have been positively associated with breast cancer risk in prospective studies. We assessed whether adding AMH and/or testosterone to the Gail model improves its prediction performance for women aged 35-50.</p><p>Methods: In a nested case-control study including ten prospective cohorts (1762 invasive cases/1890 matched controls) with pre-diagnostic serum/plasma samples, we estimated relative risks (RR) for the biomarkers and Gail risk factors using conditional logistic regression and random-effects meta-analysis. Absolute risk models were developed using these RR estimates, attributable risk fractions calculated using the distributions of the risk factors in the cases from the consortium, and population-based incidence and mortality rates. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminatory accuracy of the models with and without biomarkers.</p><p>Results: The AUC for invasive breast cancer including only the Gail risk factor variables was 55.3 (95% CI 53.4, 57.1). The AUC increased moderately with the addition of AMH (AUC 57.6, 95% CI 55.7, 59.5), testosterone (AUC 56.2, 95% CI 54.4, 58.1), or both (AUC 58.1, 95% CI 56.2, 59.9). The largest AUC improvement (4.0) was among women without a family history of breast cancer.</p><p>Conclusions: AMH and testosterone moderately increase the discriminatory accuracy of the Gail model among women aged 35-50. We observed the largest AUC increase for women without a family history of breast cancer, the group that would benefit most from improved risk prediction because early screening is already recommended for women with a family history.</p>
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4.
  • Playdon, Mary C., et al. (författare)
  • Metabolomics Analytics Workflow for Epidemiological Research : Perspectives from the Consortium of Metabolomics Studies (COMETS)
  • 2019
  • Ingår i: Metabolites. - MDPI. - 2218-1989 .- 2218-1989. ; 9:7
  • Tidskriftsartikel (refereegranskat)abstract
    • <p>The application of metabolomics technology to epidemiological studies is emerging as a new approach to elucidate disease etiology and for biomarker discovery. However, analysis of metabolomics data is complex and there is an urgent need for the standardization of analysis workflow and reporting of study findings. To inform the development of such guidelines, we conducted a survey of 47 cohort representatives from the Consortium of Metabolomics Studies (COMETS) to gain insights into the current strategies and procedures used for analyzing metabolomics data in epidemiological studies worldwide. The results indicated a variety of applied analytical strategies, from biospecimen and data pre-processing and quality control to statistical analysis and reporting of study findings. These strategies included methods commonly used within the metabolomics community and applied in epidemiological research, as well as novel approaches to pre-processing pipelines and data analysis. To help with these discrepancies, we propose use of open-source initiatives such as the online web-based tool COMETS Analytics, which includes helpful tools to guide analytical workflow and the standardized reporting of findings from metabolomics analyses within epidemiological studies. Ultimately, this will improve the quality of statistical analyses, research findings, and study reproducibility.</p>
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