scholarly journals Cohort Study of Risk Factors for Breast Cancer in Post Menopausal Women

2013 ◽  
Vol 35 ◽  
pp. e2013003 ◽  
Author(s):  
Arthur J. Hartz ◽  
Tao He
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cohort Study ◽  
Menopausal Women ◽  
Post Menopausal

scholarly journals 82P Risk factors for breast cancer: A case-control study among post-menopausal women in Pakistan

2016 ◽  
Vol 27 ◽  
pp. ix24
Author(s):  
N.A. Jadoon ◽  
M. Hussain ◽  
F.U. Sulehri ◽  
A. Zafar ◽  
A. Ijaz
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Case Control Study ◽  
Case Control ◽  
Menopausal Women ◽  
Post Menopausal ◽  
Control Study

scholarly journals A comparison of time to event analysis methods, using weight status and breast cancer as a case study

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Georgios Aivaliotis ◽  
Jan Palczewski ◽  
Rebecca Atkinson ◽  
Janet E. Cade ◽  
Michelle A. Morris
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cohort Study ◽  
Weight Status ◽  
Cox Model ◽  
Breast Cancer Incidence ◽  
Time To Event ◽  
Menopausal Women ◽  
Post Menopausal ◽  
Partial Dependence

AbstractSurvival analysis with cohort study data has been traditionally performed using Cox proportional hazards models. Random survival forests (RSFs), a machine learning method, now present an alternative method. Using the UK Women’s Cohort Study (n = 34,493) we evaluate two methods: a Cox model and an RSF, to investigate the association between Body Mass Index and time to breast cancer incidence. Robustness of the models were assessed by cross validation and bootstraping. Histograms of bootstrap coefficients are reported. C-Indices and Integrated Brier Scores are reported for all models. In post-menopausal women, the Cox model Hazard Ratios (HR) for Overweight (OW) and Obese (O) were 1.25 (1.04, 1.51) and 1.28 (0.98, 1.68) respectively and the RSF Odds Ratios (OR) with partial dependence on menopause for OW and O were 1.34 (1.31, 1.70) and 1.45 (1.42, 1.48). HR are non-significant results. Only the RSF appears confident about the effect of weight status on time to event. Bootstrapping demonstrated Cox model coefficients can vary significantly, weakening interpretation potential. An RSF was used to produce partial dependence plots (PDPs) showing OW and O weight status increase the probability of breast cancer incidence in post-menopausal women. All models have relatively low C-Index and high Integrated Brier Score. The RSF overfits the data. In our study, RSF can identify complex non-proportional hazard type patterns in the data, and allow more complicated relationships to be investigated using PDPs, but it overfits limiting extrapolation of results to new instances. Moreover, it is less easily interpreted than Cox models. The value of survival analysis remains paramount and therefore machine learning techniques like RSF should be considered as another method for analysis.

82P Risk factors for breast cancer: A case-control study among post-menopausal women in Pakistan

2016 ◽  
Vol 27 (suppl_9) ◽  
Author(s):  
N.A. Jadoon ◽  
M. Hussain ◽  
F.U. Sulehri ◽  
A. Zafar ◽  
A. Ijaz
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Case Control Study ◽  
Case Control ◽  
Menopausal Women ◽  
Post Menopausal ◽  
Control Study

scholarly journals Association of body composition with odds of breast cancer by molecular subtype: analysis of the Mechanisms for Established and Novel Risk Factors for Breast Cancer in Nigerian Women (MEND) study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tomi Akinyemiju ◽  
Kelley Jones ◽  
Anjali Gupta ◽  
Taofik Oyekunle ◽  
Veeral Saraiya ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Body Composition ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
African Women ◽  
Luminal A ◽  
Menopausal Women ◽  
Novel Risk Factors ◽  
Post Menopausal

Abstract Background The association between obesity and breast cancer (BC) has been extensively studied among US, European and Asian study populations, with often conflicting evidence. However, despite the increasing prevalence of obesity and associated conditions in Africa, the continent with the highest age-standardized BC mortality rate globally, few studies have evaluated this association, and none has examined in relation to molecular subtypes among African women. The current analysis examines the association between body composition, defined by body mass index (BMI), height, and weight, and BC by molecular subtype among African women. Methods We estimated odds ratios (ORs) and 95% confidence intervals (95% CI) for the association between measures of body composition and BC and molecular subtypes among 419 histologically confirmed cases of BC and 286 healthy controls from the Mechanisms for Established and Novel Risk Factors for Breast Cancer in Women of Nigerian Descent (MEND) case-control study. Results Higher BMI (aOR: 0.79; 95% CI: 0.67, 0.95) and weight (aOR: 0.83; 95% CI: 0.69, 0.98) were associated with reduced odds of BC in adjusted models, while height was associated with non-statistically significant increased odds of BC (aOR: 1.07, 95% CI: 0.90, 1.28). In pre/peri-menopausal, but not post-menopausal women, both higher BMI and weight were significantly associated with reduced odds of BC. Further, higher BMI was associated with reduced odds of Luminal A, Luminal B, and HER2-enriched BC among pre/peri-menopausal women, and reduced odds of triple-negative BC among post-menopausal women. Conclusions Higher BMI and weight were associated with reduced odds of BC overall and by molecular subtype among West African women. Larger studies of women of African descent are needed to definitively characterize these associations and inform cancer prevention strategies.

scholarly journals GSTM1, GSTT1, and GSTP1 polymorphisms, breast cancer risk factors and mammographic density in women submitted to breast cancer screening

2012 ◽  
Vol 15 (2) ◽  
pp. 246-255 ◽  
Author(s):  
Ernestina Silva de Aguiar ◽  
Juliana Giacomazzi ◽  
Aishameriane Venes Schmidt ◽  
Hugo Bock ◽  
Maria Luiza Saraiva-Pereira ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Mammographic Density ◽  
Mammographic Screening ◽  
Allelic Frequency ◽  
Cancer Risk Factors ◽  
Molecular Tests ◽  
Increased Risk ◽  
Menopausal Women ◽  
Post Menopausal

Genetic polymorphisms in genes related to the metabolism of xenobiotics, such as genes of the glutathione S-transferases (GSTM1, GSTT1, and GSTP1) superfamily have been associated with an increased risk for breast cancer (BC). Considering the high incidence of BC in the city of Porto Alegre in southern Brazil, the purpose of this study was to characterize genotypic and allelic frequencies of polymorphisms in GSTM1, GSTT1, and GSTP1, and correlate these molecular findings with established risk factors for breast cancer including mammographic density, in a sample of 750 asymptomatic women undergoing mammographic screening. Molecular tests were performed using the multiplex polymerase chain reaction (PCR) for GSTM1 and GSTT1, and quantitative PCR for GSTP1 polymorphisms. Overall, the frequencies of GSTM1 and GSTT1 null genotypes were 45% and 21%, respectively. For GSTP1 polymorphism, genotypic frequencies were 44% for the Ile/Ile genotype, 44% for the Ile/Val genotype, and 12% for Val/Val genotype, with an allelic frequency of 66% for the wild type allele in this population, similar to results of previous international publications. There was a statistically significant association between the combined GSTM1 and GSTT1 null genotypes (M-/T-) and mammographic density in post menopausal women (p = 0.031). When the GSTT1 null (T-) genotype was analyzed isolated, the association with mammographic density in post menopausal women and in the overall sample was also statistically significant (p = 0.023 and p = 0.027, respectively). These findings suggest an association of GSTM1 and GSTT1 null genotypes with mammographic density.

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