Type 2 diabetes, obesity, and breast cancer risk among Japanese women of the atomic bomb survivor cohort

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.

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Type 2 Diabetes and Obesity Metabolic Interactions: Common Factors for Breast Cancer Risk and Novel Approaches to Prevention and Therapy

Current Diabetes Reviews ◽

10.2174/157339912799424519 ◽

2012 ◽

Vol 8 (2) ◽

pp. 116-130 ◽

Cited By ~ 31

Author(s):

Linda Vona-Davis ◽

David P. Rose

Keyword(s):

Breast Cancer ◽

Type 2 Diabetes ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Common Factors ◽

Metabolic Interactions ◽

Novel Approaches ◽

Diabetes And Obesity ◽

Prevention And Therapy

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Sitagliptin May Reduce Breast Cancer Risk in Women With Type 2 Diabetes

Clinical Breast Cancer ◽

10.1016/j.clbc.2016.11.002 ◽

2017 ◽

Vol 17 (3) ◽

pp. 211-218 ◽

Cited By ~ 7

Author(s):

Chin-Hsiao Tseng

Keyword(s):

Breast Cancer ◽

Type 2 Diabetes ◽

Breast Cancer Risk ◽

Cancer Risk ◽

Reduce Breast Cancer Risk

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

10.2196/preprints.14027 ◽

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.

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