Objective To examine factors associated with breast cancer among adult women in suburban Shanghai and develop a risk stratification model, and to provide a basis for the identification of high-risk populations.

Methods A cross-sectional study was conducted using baseline data from the Shanghai Suburban Adult Cohort and Biobank. A total of 39 683 women aged 20-74 years, comprising 249 breast cancer cases and 39 434 non-breast cancer cases, were included. Information on demographic characteristics, reproductive health, lifestyle, chronic diseases, and psychological status was collected. Missing data were handled using multiple imputation. Multivariable Firth logistic regression analyses were used to identify factors associated with breast cancer, and restricted cubic spline analyses were performed to assess the dose-response relationship between glycated hemoglobin and breast cancer. With breast cancer as the outcome variable, the data were randomly split into a training set and a test set at a ratio of 7∶3. A logistic regression model was developed for risk stratification and further calibrated using Platt scaling. Model performance was evaluated using the area under receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve (PR-AUC), Brier score, calibration intercept, and calibration slope, to assess discrimination, overall prediction error, and calibration. Given that breast cancer was a low-prevalence outcome, the model’s precision, recall, and lift for the top 5% and top 10% of high-risk individuals were further calculated, to evaluate the model’s ability to identify and stratify risk in high-risk screening scenarios.

Results In the fully adjusted multivariable Firth logistic regression model, increasing age was associated with a higher risk of breast cancer (OR=1.050, 95%CI: 1.033-1.067), whereas older age at menarche (OR=0.921, 95%CI: 0.859-0.987) and a greater number of pregnancies (OR=0.875, 95%CI: 0.770-0.986) were associated with a lower risk of breast cancer. Women with a history of estrogen use had a higher risk of breast cancer than those without such a history (OR=3.098, 95%CI: 1.481-5.728). Hyperglycemia was associated with an increased risk of breast cancer (OR=1.754, 95%CI: 1.305-2.334). Compared with women without anxiety or depression, those with mild (OR=2.239, 95%CI: 1.484-3.266) or severe (OR=10.104, 95%CI: 1.106-32.798) anxiety or depression had a higher risk of breast cancer. Restricted cubic spline analyses showed a nonlinear association between glycated hemoglobin and breast cancer (P=0.003). The logistic regression-based risk stratification model showed moderate discriminative ability (ROC-AUC=0.730, 95%CI: 0.673-0.784) and a low overall prediction error (Brier score=0.006, 95%CI: 0.006-0.006). The lift values were 4.261 for the top 5% high-risk group and 3.199 for the top 10% high-risk group, identifying 21.3% and 32.0% of breast cancer cases, respectively.

Conclusion Breast cancer among adult women in suburban Shanghai is associated with older age, earlier age at menarche, fewer pregnancies, history of estrogen use, hyperglycemia, and mild and severe anxiety or depression, suggesting that greater attention should be paid to reproductive hormone-related factors, metabolic abnormalities, and psychological health in community-based women’s health management. The logistic regression-based model using routine epidemiological indicators shows moderate discriminative ability and potential for enriching high-risk populations and might help support initial screening and stratified management of women at high risk of breast cancer in resource-limited community settings.