Insomnia and prostate cancer risk: insights from NHANES and gene correlation analysis

Introduction

Prostate cancer, a prevalent malignancy among men, poses a substantial threat to male survival and exhibits a marked age-related increase in incidence, thereby serving as a quintessential example of an age-dependent malignancy (1). Prior to 2014, the incidence of prostate cancer had been declining, owing to advancements in clinical detection technology; however, since then, it has been on the rise, increasing by approximately 3% per year. This increase can be attributed to factors such as an aging population and the impact of over-medication prevention (2). Understanding the etiology of prostate cancer has become a crucial aspect of current research in this field. Alongside well-known risk factors such as age and family history (3), there is emerging evidence linking high body mass index (BMI) and high fasting plasma glucose (FPG) to prostate cancer (4). Identifying and addressing potential causes of prostate cancer can significantly contribute to promoting effective prevention strategies and mitigating the negative impact of this disease on individuals’ lives and overall health.

Sleep is a crucial circadian physiological process that helps maintain a normal internal environment in the body. Given the potential impact of sleep disorders on various health conditions, we explored their relationship with prostate cancer risk. Disruptions in sleep can lead to the activation of inflammatory responses, thereby impacting overall health (5). Sleep disorders can be categorized into primary and secondary sleep disorders. Primary sleep disorders encompass sleep-disordered breathing (SDB), insomnia, and various sleep-wake disorders, such as hypersomnia and narcolepsy. On the other hand, secondary sleep disorders are often caused by underlying conditions that affect sleep, such as gastroesophageal reflux disease (GERD) and asthma (6). Primary sleep disorders have been causally linked to dysfunctions in various body systems, notably hormone secretion, metabolism, and, potentially, carcinogenesis (7). Common symptoms of sleep disorders include difficulty falling asleep, difficulty staying asleep, and reduced sleep quality and recovery. It has been observed that sleep duration gradually decreases with age, which is consistent with the age profile of prostate cancer, as it is more prevalent in older men (8).

Given the overlap in the age profiles of prostate cancer and sleep disorders, and the established links between sleep disturbances and dysfunctions in multiple body systems, including hormone secretion and metabolism, which are known to play pivotal roles in prostate cancer development, we hypothesized that sleep disorders may contribute to the increased incidence of prostate cancer in older men. To test this hypothesis, we conducted a comprehensive study combining cross-sectional analysis of data from the National Health and Nutrition Examination Survey (NHANES) and Mendelian randomization (MR) analysis of genome-wide association studies (GWAS) data, aiming to elucidate the potential causal relationship between sleep disorders, particularly insomnia, and prostate cancer risk. Our findings have the potential to provide new insights into the etiology of prostate cancer and inform future prevention and intervention strategies. We present this article in accordance with the STROBE-MR reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-24-542/rc).

Methods

To assess the risk of prostate cancer in men over 40 years of age with sleep disorders, we conducted a cross-sectional study analyzing the effects of sleep disorders and their subcategories on indicators related to prostate cancer. The study population was selected from the nationally representative NHANES database, covering all states in the U.S. Because prostate cancer incidence may be age-related, we used multiple model adjustments in the study to remove the influence of age and other relevant factors on the results and validated the causal association of sleep disorders and their subcategories with prostate cancer using MR (9). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

The study population in NHANES

The NHANES database grew out of a series of surveys on a variety of health issues called the NHANES Program implemented by the National Center for Health Statistics of the Centers for Disease Control and Prevention (CDC) in the early 1960s (2). The NHANES Program is a cross-sectional survey that has been updated and released with new data every 2 years since 1999, with the ability to make adjustments each year based on the previous cycle for the adjustments are made to the research project for the new year as well as minor adjustments to previous data (3). The NHANES program consists of both an interview survey and a physical examination. The interview survey portion is conducted in the participant’s home, and all questionnaires have trained personnel to report the information. This was followed by a standardized physical examination and laboratory tests (4). This study explored sleep disorders and prostate cancer, and relevant data from the 2005–2006 and 2007–2008 cycles were selected for this study based on data stability and completeness. A total of 3,094 participants were enrolled after screening for further analysis, the specific screening process is shown in Figure S1.

The research variable and covariates

Questions on sleep (SLQ) were first published in the NHANES database in the 2005–2006 cycle, including sleep habits and sleep disorders, and were significantly reduced in the 2009–2010 cycle. In the NHANES database, insomnia was defined based on participants’ self-reported frequency and duration of difficulty initiating or maintaining sleep within the past month. Participants were asked specific questions regarding their sleep patterns, and the effects of sleep deprivation were categorized at 4-hour intervals. For this study, insomnia was considered present if participants reported frequent difficulty sleeping, irrespective of the duration categories.

Prostate cancer studies included diagnosed prostate cancer, total prostate specific antigen (tPSA) (ng/mL), free prostate specific antigen (fPSA) (ng/mL) and prostate specific antigen (PSA) ratio (%). The relevant data can be obtained from the PSA module in Laboratory Data. The tPSA, fPSA and the ratio of free to total PSA (f/t PSA) measurements are shown in the Description of Laboratory Methodology section of the website (https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2005/DataFiles/PSA_D.htm#LBXP1). Prostate cancer patients with prostate infection or without tPSA, fPSA and f/t PSA measurements. The prevalence of prostate cancer was determined using data from the NHANES 2005–2008 cycles. Participants were classified based on whether they had been told by their doctors that they had prostate cancer or not. Specifically, diagnosed prostate cancer, tPSA, fPSA, and f/t PSA were considered. Participants with prostate infection or missing PSA measurements were excluded from the analysis to ensure data accuracy.

The following variables were used as covariates to construct multivariate models: age (years); race; education level; marital status; poverty-to-income ratio, BMI, smoking status (no fewer than 100 cigarettes smoked to date); serum uric acid (µmol/L), creatinine (mmol/L), and calcium (mmol/dL) levels; hypertension; and diabetes.

Causal relationships between sleep disorder and prostate cancer risk in MR

In the context of MR analysis, single nucleotide polymorphisms (SNPs) associated with sleep disorders serve as proxies for the exposure, enabling the investigation of their potential causal impact on prostate cancer risk. The selected SNPs meet the three key assumptions of MR:

• The SNPs are robustly associated with sleep disorders, confirmed by genome-wide significance.
• They are independent of confounders due to their random allocation at conception.
• Their effect on prostate cancer is mediated solely through their influence on sleep disorders, not via other pathways.

This method allows us to infer causality with greater confidence than traditional observational studies.

Summary data for sleep disorder and prostate cancer from GWAS

To ensure the reliability of our findings regarding the causal relationship, we conducted a comprehensive search for eligible summary-level data from the largest public GWAS for each trait, as outlined in Table 1. all the data used in our study were previously published and publicly available, no additional ethical approval was required. Specifically, we extracted summary statistics for prostate cancer and different sleep disorders from the IEU Open GWAS project (https://gwas.mrcieu.ac.uk/datasets/). The diagnostic criteria and inclusion methods were consistent with the original literature.

Selection of genetic instrumental variables (IVs)

To identify suitable SNPs as IVs for sleep disorders, we selected SNPs that were strongly associated with the exposure based on genome-wide significance (P<1×10⁻⁵). The process involved several steps:

• Initial identification of SNPs related to sleep disorders.
• Clustering to ensure independence of the selected SNPs with a cutoff value of R²=0.001 and a window size of 10,000 kb [R² = 2 × (1 − EAF) × EAF × β²/(SE² × N)] (7).
• Utilization of the Phenoscanner database to screen for genetic variations associated with potential confounders (http://www.phenoscanner.medschl.cam.ac.uk/).
• Calculation of F-statistics to ensure that the selected SNPs were not weak instruments, with F values >10 indicating strong instrument relevance [F = R² × (N − k − 1)/k × (1 − R²)] (7).
• Alignment of exposure and outcome datasets to ensure allele consistency, thus maintaining the validity of the MR analysis.

After applying these screening steps, we collected the obtained SNPs as the final IVs for subsequent two-sample MR analyses (8).

Statistical analysis

Cross‑sectional study

In this study mobile examination center weight (provided by NHANES) was used as the weighted variable of the study. Continuous and categorical variables are presented as weighted means (standard errors) and frequencies (weighted percentages). Correlations between variables were determined by Pearson or Spearman’s method and weighted multiple logistic regression models were selected in the statistical approach of regression models. Two-sided P value <0.05 was considered statistically significant. All analyses were performed using R Studio version 4.3.2 (http://www.R-project.org/).

MR analysis

A combination of multiple statistical methods was employed, with inverse variance weighting (IVW) as the primary method. In addition to IVW, we used MR-Egger regression, weighted median (WM), simple mode, weighted mode, and MR multidirectional residuals and MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) as secondary methods for reference.

IVW, which includes fixed- and random-effects estimates, plays a major role in analyzing the influence of exposure factors on outcomes (9). MR-Egger regression, based on the assumption of instrumental strength independent of direct effects (InSIDE), involved weighted linear regression. The statistical significance threshold was set at P<0.05. However, due to limitations in assessing the WM of the IV estimates, we used the WM method to evaluate the overall causal effect using a large number of genetic instruments.

To investigate heterogeneity identified by the IVW and MR-Egger regression methods, we calculated the Cochran statistic. If the p-value was less than 0.05, indicating the presence of heterogeneity, we employed a random-effects model for subsequent analyses. Otherwise, a fixed-effects model was used.

To assess the potential bias of individual SNPs on the overall causal effect, we conducted “leave-one-out” sensitivity analyses. All two-sample MR analyses were conducted using the two-sample MR and MR-PRESSO packages.

Results

Baseline characteristics of the study population in NHANES

A total of 3,094 participants were included in the observational study, including 154 in the prostate cancer group and 2,940 in the non-prostate cancer group. After sample weighting, the differences between the two groups in age, marital status and creatinine levels were statistically significant (P<0.05), as shown in Table S1.

Participants were categorized based on their reported sleep duration at 4-hour intervals, and significant findings were observed primarily in the presence or absence of insomnia rather than sleep duration categories. The prevalence of prostate cancer was determined based on self-reported diagnosis and PSA measurements from the NHANES database. Differences in prevalence rates were analyzed across various demographic and clinical factors.

Association between sleep disorder and prostate cancer in NHANES

Unadjusted models results

Multifactor-adjusted weighted logistic regression models revealed that sleep disorder was associated with an increased expression of fPSA [odds ratio (OR) =1.06; 95% confidence interval (CI): 1.02–1.10; P=0.006] in the unadjusted models. Additionally, restless legs increased the risk of prostate cancer in the unadjusted model (OR =1.01; 95% CI: 1.01–1.01; P<0.001). Furthermore, feel unrested during the day in unadjusted models decreased tPSA (OR =0.85; 95% CI: 0.81–0.91; P<0.001) and fPSA (OR =0.97; 95% CI: 0.95–0.98; P<0.05). Sleep apnea in the non-adjusted model decreased the expression of tPSA (OR =0.78; 95% CI: 0.65–0.94; P=0.01) and fPSA (OR =0.95; 95% CI: 0.92–0.99; P=0.03). Regarding sleep duration, when segmented into 0–4, 4–8, and 8+ hours, it was found that in the unadjusted model, the risk of prostate cancer was reduced in those with 8+ hours of sleep compared to those with 0–4 hours of sleep (OR =0.94; 95% CI: 0.88–1.00; P=0.047). As sleep duration increased, patient tPSA gradually increased (OR =1.35, 95% CI: 1.06–1.71, P=0.02 for 4–8 hours; OR =2.62, 95% CI: 1.61–4.24, P<0.001 for 8+ hours).

Adjusted models results

In the adjustment of model 2, sleep disorder was found to increase the expression of tPSA (OR =1.25; 95% CI: 1.02–1.53; P=0.03) and fPSA (OR =1.05; 95% CI: 1.01–1.10; P=0.02), but had no significant effect on prostate carcinogenesis. For sleep apnea, in the adjustment of model 2, tPSA (OR =0.79; 95% CI: 0.63–0.98; P=0.03) and fPSA (OR =0.95; 95% CI: 0.91–0.99; P=0.03) showed a decreased expression. Insomnia was shown to increase in all three adjusted models of prostate cancer occurrence (OR =1.02, 95% CI: 1.01–1.02, P<0.001; OR =1.01, 95% CI: 1.00–1.02, P=0.021; OR =1.01, 95% CI: 1.00–1.03, P=0.04). Snore increased prostate cancer in the fully adjusted model (OR =1.00; 95% CI: 1.00–1.01; P=0.04). Wake up during night decreased fPSA in the adjustment of model 2 (OR =0.99; 95% CI: 0.98–1.00; P=0.045). In the age-adjusted model, feel unrested during the day was associated with decreased tPSA (OR =0.94; 95% CI: 0.89–0.99; P=0.02) and fPSA (OR =0.98; 95% CI: 0.97–1.00; P=0.01). Details of these findings are presented in Table 1.

Causal relationships between sleep disorder and prostate cancer risk in MR

At the P<1.0×10⁻⁵ level, we screened SNPs that could be used as IVs by using prostate cancer data (ebi-a-GCST90018905) as the outcome variable and sleep disorder related factors as exposure factors, All SNPs’ F-statistic more than 10 indicated that there were not weak IVs in the results and this analysis is reliable. Our results showed that sleeplessness/insomnia in IVW approach could increase the risk of prostate cancer development (OR =1.346; 95% CI: 1.048–1.730; P=0.02), the same result was expressed in WM (OR =1.446; 95% CI: 1.030–2.030; P=0.03). No possible association with prostate cancer development was found in other types of sleep disorders, as detailed in Table 2, the remaining process results are shown in Tables S2-S4, https://cdn.amegroups.cn/static/public/10.21037tau-24-542-1.docx. The scatter plot, leave-one-out analysis, and funnel plot of MR were presented in Figures S2-S10. In the reverse study, it was not found that prostate cancer could cause insomnia.

Discussion

In this study, we conducted a large-scale cross-sectional study using data on sleep disorders and prostate cancer from the NHANES database from 2005 to 2008, and a MR analysis of prostate cancer data from GWAS database to investigate the potential association between sleep disorders and prostate cancer. Both cross-sectional studies and MR analyses found that insomnia increased the incidence of prostate cancer. In addition, cross-sectional studies found that some types of sleep disorders caused changes in tPSA and fPSA levels, but had a smaller effect on f/t PSA, and that 0–4 hours of sleep was associated with a higher likelihood of prostate cancer compared to 4–8 and 8–12 hours of sleep, depending on the sleep duration.

With the gradual increase in research into the aetiology of prostate cancer, some of the studies related to the aetiology of prostate cancer are beginning to recognise the relationship between sleep disorder and the development of prostate cancer. The findings of this study have implications for understanding the complex interplay between sleep disorders and prostate cancer risk, potentially guiding future clinical practices and public health interventions. Melatonin, a hormone predominantly synthesized during the night in close association with darkness, plays a pivotal role in regulating physiological processes. A growing body of evidence has demonstrated that melatonin possesses antitumoral properties, particularly in the context of prostate cancer (10). As an efficacious scavenger of free radicals, melatonin may contribute to the attenuation of oxidative stress implicated in the progression of prostate cancer (11). Notably, patients with prostate cancer exhibit a significant reduction in melatonin secretion compared to males with benign prostatic hyperplasia (12). In alternative analyses, male individuals with sleep disorders typically manifested reduced levels of 6-sulfatoxymelatonin (aMT6s). Specifically, those with urinary aMT6s concentrations below the median exhibited a 47% elevated overall risk of prostate cancer, albeit this association did not reach statistical significance. Nevertheless, in the context of advanced prostate cancer, encompassing metastatic disease and prostate cancer-related mortality, males presenting with lower aMT6s levels demonstrated a markedly increased risk, with a hazard ratio of 4.04 (range, 1.26–12.98) (13).

In a prospective AGES-Reykjavik cohort study by Sigurdardottir and others involving 2,102 people, 135 people (6.4%) were diagnosed with prostate cancer during the course of the study, and patients with sleep disorders, including difficulty falling asleep and trouble staying asleep, had a significantly higher risk of prostate cancer than others. Patients with prostate cancer had a significantly increased risk compared to others (1.7, 1.0–2.9) and (2.1, 1.2–3.7), and the association between sleep disorders and prostate cancer was even stronger in patients with high-grade prostate cancer (2.1, 0.7–6.2) and (3.2, 1.1–9.7) (14). SDB may induce a hypoxic environment in the body, thereby promoting tumour proliferation (15). From this perspective, the role of sleep disorders in prostate cancer is more likely to be in the progression of existing prostate cancer than in the development of prostate cancer. In a study by Wiggins et al., it was found that people who were sleepy during the day had a lower risk of developing low-grade prostate cancer, and patients who suffered from insomnia at night had a higher risk of developing high-grade prostate cancer (16). The duration of use of sleep-related medications may also be an influential factor in the development of prostate cancer (17). A review of 10 prospective studies, 8,392 cases and 555,678 participants showed that sleep duration (short sleep duration: 1.05, 0.90–1.24; long sleep duration: 0.92, 0.76–1.12) usually has no significant effect on prostate cancer development (18), and sleep may only be protective against prostate cancer when sleep duration is significantly increased to more than 9 hours a day (19). This is similar to the results in this paper.

Multiple large-scale epidemiological studies have demonstrated that patients with sleep disorders or circadian rhythm disruptions exhibit a significantly increased risk of developing sex hormone-related cancers, such as breast cancer (20), endometrial cancer (21), ovarian cancer (22), and prostate cancer (23). This finding not only sheds light on the potential role of sleep disturbances in carcinogenesis but also offers a novel perspective for cancer prevention.

At the biological mechanism level, sleep disorders and circadian rhythm disruptions interfere with the function of the circadian clock, subsequently affecting key physiological processes such as cell proliferation and apoptosis, ultimately promoting the onset of cancer. Notably, fatty acid oxidation (FAO), a crucial metabolic process, has been found to be closely associated with sleep deprivation and cancer progression. FAO can sense the circadian disruptions induced by sleep deprivation and promote tumor growth, while regular supplementation with β-endorphin effectively reverses this process, opening up new avenues for cancer treatment (24).

In observational studies, there are several possible reasons that may help to confirm the association between sleep disorder and prostate cancer. First, inflammatory effects play an important role in the development of prostate cancer, and when sleep activity, which can boost the body’s immunity, is impaired, the body shows higher levels of inflammatory exposure (25), while chronic prostate inflammation may be a suspected risk factor for prostate cancer (26). Sleep disorders may contribute to the development of prostate cancer by affecting both inflammation and hormone levels.

In summary, our study demonstrates a significant association between insomnia and increased risk of prostate cancer, supported by both NHANES cross-sectional data and MR analysis using GWAS data. While this approach effectively mitigates confounding and reverse causation biases, there are limitations, such as potential pleiotropy, where SNPs may impact the outcome through unrelated pathways. PSA can be affected by a variety of factors such as diet and smoking (27).

However, it must be acknowledged that the present study is not without its limitations. Firstly, the data employed in this study were derived from the NHANES database, which, although comprehensive in scope, is constrained by inherent limitations. For example, the variability in the population from which the data was collected poses challenges for conducting longitudinal cohort studies using this database. Furthermore, the self-reported nature of certain variables may introduce biases and inaccuracies, as the research content primarily relies on the information provided in the survey questionnaires, potentially limiting the accuracy of insights into more specialized and nuanced issues. Additionally, given that the NHANES database is representative of the U.S. population, caution is warranted when extrapolating our findings to populations with distinct demographic, cultural, and socioeconomic characteristics. Differences in healthcare systems, lifestyle factors, and genetic backgrounds may influence the results, underscoring the necessity for future research to replicate these findings across diverse populations to enhance the external validity of the study.

Conclusions

In conclusion, our study suggests a potential correlation between insomnia and the risk of prostate cancer, with an indicative causal relationship between the two. We did not detect a causal link between sleep duration and the onset of prostate cancer; however, an extended sleep duration appears to be associated with a decreased risk of the disease, suggesting it may function as a potential protective factor. It is important to underscore that a substantial proportion of insomnia patients do not progress to prostate cancer, possibly due to the presence of unmeasured underlying confounding factors. Therefore, we report our findings conservatively and urge for further research to elucidate the intrinsic nature of this association.

Acknowledgments

We would like to thank for all the patients in this research, all the scholars in this article, and all the teammates for supporting this research. We are also particularly grateful to our colleagues at The First Affiliated Hospital of Jilin University for their contributions.

Footnote

Reporting Checklist: The authors have completed the STROBE-MR reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-24-542/rc

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-24-542/prf

Funding: This study was supported by Jilin Scientific and Technological Development Program (No. 20200201315JC), Natural Science Foundation of Jilin Province (No. 20210101272JC), Jilin Province Tianhua Health Foundation (No. J2023JKJ017), Beijing Bethune Charity Foundation (No. mnzl202022) and Key Research and Development Project in the Field of Medicine and Health of Jilin Provincial Department of Science and Technology (No. 20230204091YY).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-24-542/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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