scholarly journals Associations of obesity and circulating insulin and glucose with breast cancer risk: a Mendelian randomization analysis

2018 ◽  
Vol 48 (3) ◽  
pp. 795-806 ◽  
Author(s):  
Xiang Shu ◽  
Lang Wu ◽  
Nikhil K Khankari ◽  
Xiao-Ou Shu ◽  
Thomas J Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Type 2 Diabetes ◽  
Estrogen Receptor ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Mendelian Randomization ◽  
Menopausal Status ◽  
Inverse Association ◽  
Fasting Insulin

Abstract Background In addition to the established association between general obesity and breast cancer risk, central obesity and circulating fasting insulin and glucose have been linked to the development of this common malignancy. Findings from previous studies, however, have been inconsistent, and the nature of the associations is unclear. Methods We conducted Mendelian randomization analyses to evaluate the association of breast cancer risk, using genetic instruments, with fasting insulin, fasting glucose, 2-h glucose, body mass index (BMI) and BMI-adjusted waist-hip-ratio (WHRadj BMI). We first confirmed the association of these instruments with type 2 diabetes risk in a large diabetes genome-wide association study consortium. We then investigated their associations with breast cancer risk using individual-level data obtained from 98 842 cases and 83 464 controls of European descent in the Breast Cancer Association Consortium. Results All sets of instruments were associated with risk of type 2 diabetes. Associations with breast cancer risk were found for genetically predicted fasting insulin [odds ratio (OR) = 1.71 per standard deviation (SD) increase, 95% confidence interval (CI) = 1.26-2.31, p  =  5.09  ×  10–4], 2-h glucose (OR = 1.80 per SD increase, 95% CI = 1.3 0-2.49, p  =  4.02  ×  10–4), BMI (OR = 0.70 per 5-unit increase, 95% CI = 0.65-0.76, p  =  5.05  ×  10–19) and WHRadj BMI (OR = 0.85, 95% CI = 0.79-0.91, p  =  9.22  ×  10–6). Stratified analyses showed that genetically predicted fasting insulin was more closely related to risk of estrogen-receptor [ER]-positive cancer, whereas the associations with instruments of 2-h glucose, BMI and WHRadj BMI were consistent regardless of age, menopausal status, estrogen receptor status and family history of breast cancer. Conclusions We confirmed the previously reported inverse association of genetically predicted BMI with breast cancer risk, and showed a positive association of genetically predicted fasting insulin and 2-h glucose and an inverse association of WHRadj BMI with breast cancer risk. Our study suggests that genetically determined obesity and glucose/insulin-related traits have an important role in the aetiology of breast cancer.

scholarly journals Coffee consumption and risk of breast cancer: a Mendelian Randomization study

2020 ◽  
Author(s):  
Merete Ellingjord-Dale ◽  
Nikos Papadimitriou ◽  
Michalis Katsoulis ◽  
Chew Yee ◽  
Niki Dimou ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Random Effects ◽  
Mendelian Randomization ◽  
Coffee Consumption ◽  
Sensitivity Analyses ◽  
European Ancestry ◽  
Inverse Association ◽  
Er Positive

AbstractBackgroundObservational studies have reported either null or weak protective associations for coffee consumption and risk of breast cancer.MethodsWe conducted a two-sample Mendelian randomization randomization (MR) analysis to evaluate the relationship between coffee consumption and breast cancer risk using 33 single-nucleotide polymorphisms (SNPs) associated with coffee consumption from a genome-wide association (GWA) study on 212,119 female UK Biobank participants of White British ancestry. Risk estimates for breast cancer were retrieved from publicly available GWA summary statistics from the Breast Cancer Association Consortium (BCAC) on 122,977 cases (of which 69,501 were estrogen receptor (ER)-positive, 21,468 ER-negative) and 105,974 controls of European ancestry. Random-effects inverse variance weighted (IVW) MR analyses were performed along with several sensitivity analyses to assess the impact of potential MR assumption violations.ResultsOne cup per day increase in genetically predicted coffee consumption in women was not associated with risk of total (IVW random-effects; odds ratio (OR): 0.91, 95% confidence intervals (CI): 0.80-1.02, P: 0.12, P for instrument heterogeneity: 7.17e-13), ER-positive (OR=0.90, 95% CI: 0.79-1.02, P: 0.09) and ER-negative breast cancer (OR: 0.88, 95% CI: 0.75-1.03, P: 0.12). Null associations were also found in the sensitivity analyses using MR-Egger (total breast cancer; OR: 1.00, 95% CI: 0.80-1.25), weighted median (OR: 0.97, 95% CI: 0.89-1.05) and weighted mode (OR: 1.00, CI: 0.93-1.07).ConclusionsThe results of this large MR study do not support an association of genetically predicted coffee consumption on breast cancer risk, but we cannot rule out existence of a weak inverse association.

The performance of artificial neural network, logistic regression, and random forest models to predict breast cancer in patients with type 2 diabetes mellitus (Preprint)

2019 ◽  
Author(s):  
Chia-Hung Kao
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Type 2 Diabetes Mellitus ◽  
Logistic Regression ◽  
Breast Cancer Risk ◽  
Cancer Risk

BACKGROUND Breast cancer incidence may be higher among patients with type 2 diabetes mellitus (T2DM) compared with the general population. This study evaluated the performance of three models for predicting breast cancer risk in patients with T2DM. OBJECTIVE This study evaluated the performance of three models for predicting breast cancer risk in patients with T2DM. METHODS In total, 1,267,867 patients with newly diagnosed T2DM between 2000 and 2012 were identified from Taiwan National Health Insurance Research Database. By employing their data, we created prediction models for detecting an increased risk of subsequent breast cancer development in T2DM patients. The available potential risk factors for breast cancer were also collected for adjustment in the analyses. The Synthetic Minority Oversampling Technique (SMOTE) was used to augment data points in the minority class. Each data point was randomly allocated to the training and test sets at a ratio of approximate 39:1. The performance of artificial neural network (ANN), logistic regression (LR), and random forest (RF) models were determined using the recall, precision, F1 score, and area under receiver operating characteristic curve (AUC). RESULTS The AUCs of all three models were significantly higher than the area of 0.5 for the null hypothesis (0.959, 0.865, and 0.834 for RF, ANN, and LR models, respectively). The RF model has the largest AUC among all models; moreover, it had the highest values in all other metrics. CONCLUSIONS Although all three models could accurately predict high breast cancer risk in patients with T2DM in Taiwan, the RF model demonstrated the best performance. CLINICALTRIAL This is not a chinical trial.

scholarly journals ADRB2 G–G haplotype associated with breast cancer risk among Hispanic and non-Hispanic white women: interaction with type 2 diabetes and obesity

2012 ◽  
Vol 23 (10) ◽  
pp. 1653-1663 ◽  
Author(s):  
Avonne Connor ◽  
Richard N. Baumgartner ◽  
Richard A. Kerber ◽  
Elizabeth O’Brien ◽  
Shesh N. Rai ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Type 2 Diabetes ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
White Women ◽  
Diabetes And Obesity ◽  
Hispanic White

scholarly journals Genetic Susceptibility to Type 2 Diabetes and Breast Cancer Risk in Women of European and African Ancestry

2012 ◽  
Vol 21 (3) ◽  
pp. 552-556 ◽  
Author(s):  
Ningqi Hou ◽  
Yonglan Zheng ◽  
Eric R. Gamazon ◽  
Temidayo O. Ogundiran ◽  
Clement Adebamowo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Type 2 Diabetes ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Genetic Susceptibility ◽  
African Ancestry

scholarly journals Type 2 diabetes and obesity in midlife and breast cancer risk in the Reykjavik cohort

2019 ◽  
Vol 30 (10) ◽  
pp. 1057-1065
Author(s):  
Gertraud Maskarinec ◽  
Álfheiður Haraldsdóttir ◽  
Kristjana Einarsdóttir ◽  
Thor Aspelund ◽  
Laufey Tryggvadóttir ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Type 2 Diabetes ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Diabetes And Obesity
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