Introduction

Across the globe, breast cancer emerges as the primary malignancy affecting females, holding the position as the foremost contributor to oncological fatalities within this population, thus severely impacting the health and longevity of women1. The latest 2020 statistics estimate that approximately 2.3 million individuals were diagnosed with breast cancer, resulting in approximately 685,000 fatalities. Over half of these diagnoses and two-thirds of the related deaths are reported in the less developed regions, with an anticipated surge in cases in areas undergoing economic transformation1,2. Global research efforts have sought to delineate the etiology of breast cancer, revealing it to be a multifaceted disease influenced by genetic factors, lifestyle, environmental exposures, and their interplay3. A myriad of lifestyle factors, including but not limited to body composition, obesity, physical activity levels, alcohol intake, smoking habits, dietary patterns, and vitamin intake, have been definitively linked to the risk and advancement of breast cancer4,5,6. In recent years, scientific inquiry has increasingly concentrated on how personal lifestyle behaviors affect both the occurrence and evolution of diseases. Issues such as insomnia, insufficient sleep, and changes in circadian rhythms are becoming increasingly common7,8. Consequently, researchers are committed to conducting comprehensive analyses of how different sleep patterns affect the initiation and advancement of tumors.

Sleep disorders are prevalent across the general population, with insomnia affecting 6% to 20%9, hypersomnia, defined as sleeping for more than 9 h, ranges between 0.5% and 1.6%10, 9% to 38% of people having obstructive sleep apnea11, and circadian rhythm sleep–wake disorders estimated at approximately 3%, albeit sometimes reaching up to 10%12. After reviewing experimental and epidemiological data, the WHO International Agency for Research on Cancer connected shift employment that throws off circadian rhythms to a “possible carcinogen for humans”13. Recent investigations have shown that exposure to nocturnal light increases women's chances of developing estrogen receptor-positive breast cancer, underscoring the necessity for more comprehensive studies on various breast cancer types14. Moreover, a recent analysis exploring the reciprocal link between breast cancer and disruption of the circadian rhythm found that changes in circadian rhythms and lack of sleep might trigger carcinogenic effects, regardless of nighttime light exposure. The review also highlighted the heterogeneity of research findings and the reliance on subjective or inadequate sleep metrics15.

Chronotype, an individual's natural preference for sleep and wake times within a 24-h cycle16, is shaped by genetic factors such as polymorphisms in clock genes, sex, age, and environmental factors like light exposure and social schedules17,18,19. It is typically categorized into morning types, evening types, and intermediate types. Evening types, as opposed to morning types, tend to have shorter and more irregular sleep patterns and may engage in riskier behaviors such as smoking and heavy drinking20,21, which can negatively impact health. Studies have indicated a higher prevalence of evening chronotype among patients diagnosed with gastroenteropancreatic neuroendocrine tumors22, while a morning chronotype has been linked to a lower risk of various cancers, including ovarian, endometrial, prostate, and lung cancer23,24,25,26.

Mendelian Randomization (MR) analysis, an epigenetic research strategy, employs single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to ascertain the causal influence of exposures on outcomes. This method mirrors randomized controlled trials by leveraging the random distribution of alleles during gametogenesis, thus minimizing the impact of confounding variables and reverse causality, a common challenge in conventional observational multivariate regression. This research employs a two-sample MR approach to explore, without presuppositions, the causative links between seven self-reported sleep phenotypes [morning/evening person (chronotype), morning person (chronotype), daytime dozing/sleepiness , getting up in the morning, sleeplessness, sleep duration, nap during the day] and both overall breast cancer and its subtypes [luminal A-like (ER+ and/or PR+, HER2−, grade 1 & 2); luminal B, HER2-negative-like (ER+ and/or PR+, HER2−, grade 3); luminal B-like (ER+ and/or PR+, HER2+); HER2-enriched-like (ER− and PR−, HER2+); Triple negative-like (ER−, PR−, HER2−)] among European women.

Materials and methods

Exposure data

The GWAS data on sleep phenotypes, including morning/evening person (chronotype), daytime dozing, getting up in the morning, sleeplessness, sleep duration, and nap during the day, were derived from female participants in the UK Biobank, accompanied by additional data for the morning person (chronotype) phenotype sourced from 23andMe datasets27. The study on chronotype utilized self-reported diurnal preferences, providing options such as “Clearly a morning person,” “Leans more towards morning than evening,” “Leans more towards evening than morning,” and “Clearly an evening person,” with alternatives “Unsure” or “Choose not to disclose.” Daytime dozing was evaluated based on participants' reported frequency of unintended sleep episodes during daily activities such as working, reading, or driving. Assessments of getting up in the morning were conducted via responses to the question, “Generally, how effortless is it for you to rise in the morning?” While sleeplessness was assessed through participants' difficulties in initiating and maintaining sleep, considering variations over the preceding four weeks, Daytime napping, regarded as a supplemental sleep session, and sleep duration, recorded as average hours slept within a 24-h period including naps, were both evaluated via self-report, revealing an average sleep duration of 7.2 h per day. Data on sleep characteristics is detailed in Supplementary Table 1.

We employed single nucleotide polymorphisms (SNPs) with a statistically significant threshold (P < 5 × 10–8; linkage disequilibrium (LD) r2 < 0.001, LD distance > 1 Mb) that are closely linked to sleep traits to create IVs. The F-statistic measures the relationship's strength between SNPs and sleep traits, dependent on the exposure's explained variance (R^2), sample size (n), and the count of SNPs (k), calculated as F = [(n − k − 1)/k] / [R2 / (1 − R2)]. An F-statistic above 10 suggests the chosen SNPs are potent predictors of sleep traits. To eliminate confounding bias, SNPs linked to breast cancer (threshold P < 5 × 10–8) and factors known to influence breast cancer risk—body mass index, waist circumference, age at menarche, birth weight, physical activity, alcohol consumption, smoking habits, and diabetes—were excluded, leveraging the Phenoscanner website: http://www.phenoscanner.medschl.cam.ac.uk/. Detailed data for SNPs is presented in Supplementary Table 2.

Outcome data

The BCAC provided the summarized genetic association information for breast cancer and its subtypes, encompassing the OncoArray and iCOGS datasets28, along with the aggregated results from additional GWAS for overall breast cancer. An analysis involving 133,384 breast cancer cases and 113,789 controls provided comprehensive statistics for both overall breast cancer and its five distinct subtypes. Data on breast cancer is detailed in Supplementary Table 3.

Two sample MR

In this study, we employed the “Two Sample MR” package within R Studio version 4.3.1 to conduct MR analysis, aiming to investigate potential causal links between specific genetic variants (“exposures”) and health outcomes (“outcomes”). During the data analysis process, allele harmonization was conducted to ensure data consistency by aligning the effect alleles in both the exposure and outcome datasets, guaranteeing that the effect alleles were the same across both datasets. The frequency of the effect alleles was also utilized to assist in this harmonization process.

Analysis Methods Employed: Inverse Variance Weighted (IVW): The predominant method in MR analysis, IVW utilizes a weighted average for estimates, ideal for scenarios where genetic variations influence outcomes exclusively through exposure; MR Egger: This method detects and corrects for genetic pleiotropy, making it apt for intricate scenarios involving confounders. Weighted Median: Offers median-based estimates, mitigating the undue impact of individual variants, suitable for highly heterogeneous contexts; Maximum Likelihood: Appropriate for dissecting complex relationships and providing the likeliest causation estimates.

Sensitivity and Robustness Analyses: Cochran’s Q Test and MR-Egger Intercept Analysis: Employed to identify heterogeneity and genetic pleiotropy, thus assessing the soundness of model premises; Leave-One-Out Analysis: Evaluates result stability by iteratively removing variants; Steiger Test: Verifies that genetic variations primarily influence exposures rather than outcomes, confirming instrumental variable integrity; Outlier Identification and Removal: The MR Radial approach identifies outliers via significant p-values (< 0.05) from Cochran’s Q test and MR-Egger intercept, with subsequent re-analysis bolstering result precision and dependability.

This research, leveraging the Mendelian Randomization framework, delivers a detailed and accurate exploration of causal dynamics through diverse statistical techniques and sensitivity checks. The integrated application of these methodologies not only solidifies the findings' dependability but also deepens the comprehension of intricate genetic-environmental interplays.

Results

We identified SNPs linked to seven sleep traits as exposures and six breast cancer variants as outcomes. The F-statistics of these genetic tools surpass the widely accepted benchmark of 10, signifying their robustness. Our MR analysis explored the genetic impact of sleep traits on breast cancer risk, uncovering specific sleep characteristics that affect the development of various breast cancer subtypes. Figure 1 outlines the methodology of our study.

Figure 1
figure 1

Flowchart of this two-sample MR analysis.

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Our findings suggest that chronotype, encompassing both morning and evening types, is positively correlated with overall breast cancer risk, establishing it as a risk factor. IVW and Maximum Likelihood methods yielded an OR of 1.19 with a 95% CI of 1.03–1.37 (p = 0.02) and 1.05–1.35 (p = 0.005), respectively. Cochran's Q test produced a P-value over 0.05, suggesting no heterogeneity, as did the MR-Egger intercept (Fig. 2). Leave-One-Out Analysis detected no outliers, and the Steiger Test negated reverse causation (Fig. 3, Supplementary Table 4). However, the chronotype's association with other breast cancer subtypes lacked statistical significance, as detailed in Supplementary Table 5.

Figure 2
figure 2

MR findings on the impact of sleep traits on breast cancer.

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Figure 3
figure 3

(a) Leave-one-out analysis of the causal effect between morning/evening person (chronotype) chronotype and overall breast cancer; (b) Leave-one-out analysis of the causal effect between morning person (chronotype) and overall breast cancer; (c) Leave-one-out analysis of the causal effect between morning person (chronotype) and luminal B, HER2-negative-like; (d) Leave-one-out analysis of the causal effect between daytime dozing and overall breast cancer; (e) Leave-one-out analysis of the causal effect between daytime dozing and luminal B-like; (f) Leave-one-out analysis of the causal effect between daytime dozing and luminal A-like; (g) Leave-one-out analysis of the causal effect between daytime dozing and HER2-enriched-like; (h) Estimation of the Steiger direction test from seven sleep phenotypes to breast cancer.

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Conversely, being a morning person (chronotype) showed a protective effect against overall breast cancer and luminal B, HER2-negative-like. The IVW method yielded odds ratios (OR) of 0.95 (95% CI = 0.91–0.99, p = 0.01) and 0.86 (95% CI = 0.78–0.94, p = 0.001), respectively, with the Maximum Likelihood method corroborating these findings. Despite initial indications of heterogeneity by Cochran's Q test (p < 0.05) regarding the morning person (chronotype) in relation to overall breast cancer, the removal of outliers (“rs1061032,” “rs11545787,” “rs12969848,” “rs662094,” and “rs7626335”) via the MR Radial method led to consistent results, affirming no heterogeneity or pleiotropy (Fig. 4). Leave-One-Out Analysis further verified that no individual SNPs substantially swayed the outcomes, and the Steiger Test dismissed the possibility of reverse causation (Fig. 3, Supplementary Table 4). The comprehensive analysis is available in Supplementary Table 5.

Figure 4
figure 4

(a) MR radial plots to visualize outliers in morning person (chronotype) and overall breast cancer; (b) MR radial plots to visualize outliers in morning person (chronotype) and luminal A-like.

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Additionally, daytime dozing exhibited a protective correlation against overall breast cancer, including specific subtypes such as luminal A-like, luminal B-like, and HER2-enriched-like cancers. IVW analysis yielded OR indicating significant negative correlations: 0.3 (95% CI = 0.16–0.55, p = 0.0001), 0.37 (95% CI = 0.17–0.84, p = 0.02), 0.14 (95% CI = 0.02–0.84, p = 0.03), and 0.05 (95% CI = 0.00–0.69, p = 0.03), respectively. Both Weighted Median and Maximum Likelihood methods consistently supported this negative correlation. Furthermore, the Maximum Likelihood method confirmed daytime dozing-consistent protective association with these breast cancer subtypes.

Other factors like getting up in the morning, sleeplessness, sleep duration, and daytime napping do not exhibit a statistically significant relationship with either overall breast cancer or its subtypes, suggesting no correlation.

Discussion

In this study, our objective is to utilize the MR framework approach to evaluate the influence of seven sleep patterns on breast cancer risk. We discovered a positive correlation between chronotype and the overall occurrence of breast cancer. In contrast, individuals identified as morning persons (chronotype) exhibit an effect of protection against overall breast cancer and the specific subtype of luminal B, HER2-negative-like. Furthermore, daytime dozing shows a protective association with overall breast cancer, as well as with luminal A-like, luminal B-like, and HER2-enriched-like subtypes.

Prior research has identified an inverse correlation between morning chronotype preference and overall breast cancer risk, utilizing MR analysis29. Our investigation further applies the MR approach to explore the connections between the chronotype, morning person (chronotype), and five specific breast cancer subtypes. Multiple investigations in recent times have scrutinized the connection between alterations in circadian patterns and the incidence of breast cancer. A detailed meta-analysis encompassing 28 research pieces indicated that disturbances in circadian patterns markedly increase women's susceptibility to breast cancer30; additional, contemporary research indicates that nighttime light exposure increases the likelihood of developing estrogen receptor-positive breast cancer14. Our MR findings corroborate these observational studies, emphasizing the protective inverse correlation between the morning person (chronotype) and overall breast cancer as well as the luminal B, HER2-negative-like subtype.

Morning types typically maintain regular sleep schedules by retiring to bed and rising early, which stabilizes circadian rhythms and improves sleep quality. They are more inclined to adopt healthy dietary habits, exercise regularly, and have lower BMI31,32, reducing cancer risk. Conversely, evening types often exhibit unhealthy behaviors, such as smoking, alcohol consumption, and high-sugar, high-fat diets20,21,33. They are more exposed to nighttime light, suppressing melatonin and disrupting sleep, leading to chronodisruption34. Additionally, evening types experience more social jet lag due to misalignment between their biological clocks and social schedules, resulting in chronic sleep deprivation and circadian disruption34,35. Disruption of circadian rhythms and subsequent reduced melatonin levels may contribute to breast cancer development. Melatonin is essential for regulating the circadian rhythm and significantly impacting tumor genesis and progression. A reduction in melatonin has also been observed in breast cancer36. Furthermore, female plasma estrogen levels, which melatonin significantly inhibits by blocking estrogen receptor transcriptional activity, are closely linked to breast cancer risk. Melatonin's interaction with estrogen receptors reduces their activation and suppresses the production of enzymes like aromatase, essential for estrogen synthesis15. Additionally, melatonin's antioxidant capabilities protect DNA from damage, eliminate reactive oxygen species, ensure genomic integrity, foster DNA repair, enhance mitochondrial respiratory function, and prevent mitochondrial autophagy and telomerase activity, thus aiding in tumor suppression. Melatonin also upregulates p53 protein expression, encourages its phosphorylation, deters cell proliferation, facilitates apoptosis, and lowers endothelin-1 and vascular endothelial growth factor levels, which are crucial for the proliferation and spread of tumors37. The chronotype's link to DNA methylation at specific gene loci (e.g., BACH2, JRK, and RPS6KA2) further underscores its connection to cancer development38. Beyond the direct link to breast cancer, research indicates a broader association between circadian rhythms and various chronic conditions and unhealthy behaviors, potentially affecting breast cancer risk39,40.

This research marks the inaugural investigation into the causal relationship between daytime dozing and both general as well as specific subtypes of breast cancer. Retrospective studies have found an association between frequent daytime sleepiness and a higher incidence of colorectal cancer41. Daytime sleepiness is a primary symptom of chronic sleep insufficiency and various primary sleep disorders, including narcolepsy, sleep apnea, and circadian rhythm disturbances42,43,44. GWAS of self-reported daytime dozing has identified specific genetic variations, such as those in the PATJ and PLCL1 genes, which increase the tendency for morning chronotype. Genotyping and weighted genetic risk score (GRS) analyses have shown that gene variants related to sleepiness fall into two main categories: sleep propensity and sleep fragmentation. The sleep propensity GRS is significantly linked to morning types, indicating that circadian rhythms play a crucial role in sleep drive. Additionally, genes at these loci are highly expressed in brain tissues, suggesting their involvement in morning chronotype by influencing brain mechanisms that control sleep. The study also observed genetic correlations between daytime sleepiness and both coronary heart disease and mental health traits, while there was a consistent negative genetic correlation with reproductive characteristics, specifically age at first menstruation and first childbirth45. There is a close relationship between sleep and tumors, with molecular mechanisms involving central nervous system regulation, inflammatory responses, and changes in hormones and metabolism46. Further studies are essential to clarify daytime dozing and cancer's intricate biological connections. This research first introduces a genetic link between daytime dozing and reduced breast cancer comorbidity, encouraging deeper investigation into breast cancer risk factors and novel therapeutic strategies.

This research boasts several strengths, notably employing MR to reduce confounders via genetic mediators, thereby enhancing causal inference. Data were sourced from three extensive datasets without overlapping cohorts, ensuring the genetic instruments' robustness due to the considerable GWAS sample size, which bolsters analytical precision. We applied diverse sensitivity analyses to affirm the results’ validity, mitigating bias and the risk of reverse causality. Nonetheless, this study faces limitations, including the potential impact of genetic structure variations across different demographics on MR study outcomes. The GWAS data were solely obtained from European populations, potentially restricting the applicability of our conclusions to other groups. Moreover, our analysis is based on self-reported sleep traits rather than objective measurements, introducing a possible source of bias. While this study delineates a genetic link between sleep traits and breast cancer risk, pinpointing the exact biological processes remains elusive, necessitating additional investigation.

Conclusion

Overall, this research concludes that chronotype, encompassing both morning and evening types, is linked to a higher risk of breast cancer, whereas identifying as morning person (chronotype) diminishes not only the overall risk but potentially also the risk of specific subtypes, corroborating the results of observational studies. Additionally, it presents the novel finding of a causal link between daytime dozing and breast cancer, offering fresh perspectives on the interplay between daytime dozing and cancer development. The biological mechanisms behind this correlation warrant additional exploration.