Breast cancer is a leading cause of cancer death globally, with increasing incidence and mortality rates in both developed and developing countries. Risk factors include lifestyle changes, late marriage, late first childbirth, and hormone replacement therapy (in developed countries), and lack of awareness/knowledge, inadequate screening, and delayed diagnosis (in developing countries). Previous research on the association between sleep duration, depression, and breast cancer has yielded inconsistent results. Some studies suggest an association between sleep deprivation and depression and an increased risk of breast cancer, while others show no significant relationship. This study aimed to explore this relationship using the nationally representative NHANES dataset and to develop machine learning algorithms to predict breast cancer risk.

The literature regarding the relationship between sleep duration and breast cancer risk is conflicting. Some studies have shown an increased risk of breast cancer associated with both short and long sleep durations, while others found no association. Similarly, studies on the association between depression and breast cancer have yielded mixed results. However, a systematic review and meta-analysis highlighted the critical role of depression/anxiety as an independent predictor of breast cancer recurrence and survival. A Mendelian randomization study suggested a causal link between genetically predicted depression and increased breast cancer risk.

This cross-sectional study analyzed data from five cycles of NHANES (2009-2018), including 1789 participants, of whom 263 had breast cancer. Sleep duration was self-reported, and depression was assessed using the PHQ-9 questionnaire (a score ≥10 indicating depression). Covariates included age, sex, race, education, marital status, family income-to-poverty ratio (PIR), BMI, smoking status, alcohol consumption, hypertension, and diabetes. Multivariate logistic regression was used to assess the association between sleep duration, depression, and breast cancer. Six machine learning algorithms (AdaBoost, Random Forest, Boost Tree, Artificial Neural Network, XGBoost, and Support Vector Machine) were employed to predict breast cancer risk. Random forest interpolation was used to handle missing data for hypertension and alcohol consumption.

BMI, race, and smoking status differed significantly between breast cancer and control groups. Multivariate logistic regression analysis showed a significant association between depression and breast cancer (OR = 1.99, 95% CI: 1.55–3.51). No significant association was found between sleep duration and breast cancer risk after adjusting for covariates. Compared with 7–9 hours of sleep, the odds ratios for <7 hours and >9 hours were 1.25 (95% CI: 0.85–1.37) and 1.05 (95% CI: 0.95–1.15), respectively. Among the machine learning algorithms, AdaBoost demonstrated the best predictive performance for breast cancer, with an AUC of 0.84 (95% CI: 0.81–0.87).

The findings support a significant association between depression and increased breast cancer risk, consistent with some previous studies. The mechanisms underlying this association may involve increased levels of proinflammatory cytokines and chronic systemic inflammation. The lack of a significant association between sleep duration and breast cancer risk in this study contrasts with some previous findings, but aligns with other studies. This discrepancy may be due to differences in study populations, methodologies, or the inclusion of other sleep factors. The strong performance of the AdaBoost model in predicting breast cancer highlights the potential of machine learning in risk assessment and personalized medicine.

This study demonstrated a significant association between depression and increased breast cancer risk, but no significant association between sleep duration and breast cancer risk. AdaBoost proved to be a highly effective machine learning model for breast cancer prediction. Further research using prospective studies with larger samples and objective sleep measures is needed to confirm these findings and elucidate the underlying mechanisms.

This study's cross-sectional design limits the ability to infer causality. Self-reported sleep duration may be subject to recall bias. Other factors influencing sleep quality and the immune system were not considered. While several potential confounders were controlled for, residual confounding remains possible.