Association between sleep duration, depression and breast cancer in the United States: a national health and nutrition examination survey analysis 2009–2018

Abstract

Objective

Breast cancer is the most common cancer in women, threatening both physical and mental health. The epidemiological evidence for association between sleep duration, depression and breast cancer is inconsistent. The aim of this study was to determine the association between them and build machine-learning algorithms to predict breast cancer.

Methods

A total of 1,789 participants from the National Health and Nutrition Examination Survey (NHANES) were included in the study, and 263 breast cancer patients were identified. Sleep duration was collected using a standardized questionnaire, and the Nine-item Patient Health Questionnaire (PHQ-9) was used to assess depression. Logistic regression yielded multivariable-adjusted breast cancer odds ratios (OR) and 95% confidence intervals (CI) for sleep duration and depression. Then, six machine learning algorithms, including AdaBoost, random forest, Boost tree, artificial neural network, limit gradient enhancement and support vector machine, were used to predict the development of breast cancer and find out the best algorithm.

Results

Body mass index (BMI), race and smoking were statistically different between breast cancer and non-breast cancer groups. Participants with depression were associated with breast cancer (OR = 1.99, 95%CI: 1.55–3.51). Compared with 7–9h of sleep, the ORs for <7 and >9 h of sleep were 1.25 (95% CI: 0.85–1.37) and 1.05 (95% CI: 0.95–1.15), respectively. The AdaBoost model outperformed other machine learning algorithms and predicted well for breast cancer, with an area under curve (AUC) of 0.84 (95%CI: 0.81–0.87).

Conclusions

No significant association was observed between sleep duration and breast cancer, and participants with depression were associated with an increased risk for breast cancer. This finding provides new clues into the relationship between breast cancer and depression and sleep duration, and provides potential evidence for subsequent studies of pathological mechanisms.

Keywords:

• Breast cancer
• sleep duration
• depression
• machine learning
• prediction

Author contributions statement

The conception and design: Haiyu Wang; analysis and interpretation of the data: Wei Zhu; the drafting of the paper: Yufan Cai and Yizhou Zhaoxiong; revising it critically for intellectual content: Yufan Cai; and the final approval of the version to be published: all authors; and that all authors agree to be accountable for all aspects of the work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All the data could be accessed from the website of the American Centers for Disease Control and Prevention (https://www.cdc.gov/nchs/nhanes).

Additional information

Funding

This study did not receive any funding in any form.