scholarly journals Rosiglitazone reduces breast cancer risk in Taiwanese female patients with type 2 diabetes mellitus

Oncotarget ◽  
2016 ◽  
Vol 8 (2) ◽  
pp. 3042-3048 ◽  
Author(s):  
Chin-Hsiao Tseng
Keyword(s):  
Breast Cancer ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Female Patients

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 Hypomethylation of IL1RN and NFKB1 genes is linked to the dysbalance in IL1β/IL-1Ra axis in female patients with type 2 diabetes mellitus

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0233737
Author(s):  
Sona Margaryan ◽  
Eva Kriegova ◽  
Regina Fillerova ◽  
Veronika Smotkova Kraiczova ◽  
Gayane Manukyan
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Female Patients

PO119 IMMUNE REACTIVITY IN PATIENTS WITH BREAST CANCER ASSOCIATED WITH TYPE 2 DIABETES MELLITUS

The Breast ◽  
2013 ◽  
Vol 22 ◽  
pp. S60
Author(s):  
Natalia Botnariuc ◽  
Larisa Sofroni ◽  
Vasile Jovmir ◽  
Diana Tcaciuc ◽  
Valentina Stratan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Immune Reactivity

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 The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus

Cancers ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1751 ◽  
Author(s):  
Meng-Hsuen Hsieh ◽  
Li-Min Sun ◽  
Cheng-Li Lin ◽  
Meng-Ju Hsieh ◽  
Chung Hsu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Characteristic Curve ◽  
Small Population ◽  
Research Database ◽  
Artificial Intelligence Models ◽  
Artificial Neural Network Ann

Objective: Early reports indicate that individuals with type 2 diabetes mellitus (T2DM) may have a greater incidence of breast malignancy than patients without T2DM. The aim of this study was to investigate the effectiveness of three different models for predicting risk of breast cancer in patients with T2DM of different characteristics. Study design and methodology: From 2000 to 2012, data on 636,111 newly diagnosed female T2DM patients were available in the Taiwan’s National Health Insurance Research Database. By applying their data, a risk prediction model of breast cancer in patients with T2DM was created. We also collected data on potential predictors of breast cancer so that adjustments for their effect could be made in the analysis. Synthetic Minority Oversampling Technology (SMOTE) was utilized to increase data for small population samples. Each datum was randomly assigned based on a ratio of about 39:1 into the training and test sets. Logistic Regression (LR), Artificial Neural Network (ANN) and Random Forest (RF) models were determined using recall, accuracy, F1 score and area under the receiver operating characteristic curve (AUC). Results: The AUC of the LR (0.834), ANN (0.865), and RF (0.959) models were found. The largest AUC among the three models was seen in the RF model. Conclusions: Although the LR, ANN, and RF models all showed high accuracy predicting the risk of breast cancer in Taiwanese with T2DM, the RF model performed best.

scholarly journals The association between type 2 diabetes mellitus and bladder cancer risk among postmenopausal women

2020 ◽  
Vol 31 (5) ◽  
pp. 503-510
Author(s):  
Yueyao Li ◽  
Michael S. Hendryx ◽  
Pengcheng Xun ◽  
Ka He ◽  
Aladdin H. Shadyab ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Bladder Cancer ◽  
Type 2 Diabetes Mellitus ◽  
Cancer Risk ◽  
Postmenopausal Women ◽  
Bladder Cancer Risk
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