Sleep Reduction and Depressive Symptoms Synergistically Increase Cancer Risk in Middle-Aged and Older Chinese Adults: Evidence from the CHARLS Cohort

Sleep Reduction and Depressive Symptoms Synergistically Increase Cancer Risk in Middle-Aged and Older Chinese Adults: Evidence from the CHARLS Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Sleep Reduction and Depressive Symptoms Synergistically Increase Cancer Risk in Middle-Aged and Older Chinese Adults: Evidence from the CHARLS Cohort Zhenwei Jiang, Ke Hu, Hechun Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7503440/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The independent and joint effects of sleep reduction and depression on cancer risk remain understudied in Chinese populations, particularly regarding synergistic mechanisms. This study aims to examine the impact of sleep reduction (≤ 6 hours) and depressive symptoms on cancer incidence among middle-aged and older adults in China. We will analyze both the independent and combined effects of these two factors, in order to provide important insights for understanding cancer etiology and informing prevention strategies. Methods The study utilized robust longitudinal data (2011–2020) from 14,349 cancer-free adults aged ≥ 45 years in the China Health and Retirement Longitudinal Study (CHARLS). We meticulously examined associations between baseline sleep duration (≤ 6h vs. >6h), depressive symptoms (CESD- 10 < 10 was defined as no depressive symptoms, 10 ≤ CESD- 10 <15 was classified as mild depressive symptoms, and CESD-10 ≥ 15 was classified as moderate to severe depressive symptoms.), and incident cancer. Our Cox models quantified hazard ratios (HRs) adjusted for sociodemographic, behavioural, and clinical confounders. We also performed additive interaction (RERI/AP/S) and mediation analyses, ensuring a comprehensive understanding of the data. Results Over 96.7 ± 23.88 months, 418 cancer cases occurred. Sleep reduction (≤ 6h) independently increased cancer risk by 33% (adjusted HR = 1.33, 95% CI:1.03–1.73), while moderate-severe depression increased risk by 49% (HR = 1.49, 1.08–2.07). When these two factors coexisted, they demonstrated synergistic effects, meaning their combined impact was greater than the sum of their individual effects: the combined risk was 59% higher (HR = 1.59, 1.13–2.22) with significant additive interaction (RERI = 0.34, 95% CI:0.05–0.63; AP = 21.4%, 0.03–0.39). Depression mediated only 16.5% (95% CI: -22.7-97.4) of the sleep-cancer association. Subgroups at highest risk included females, adults < 60 years, and those without hypertension/diabetes. Conclusion Sleep reduction and depression (especially moderate to severe depression) independently increase cancer risk in middle-aged/older Chinese adults, with significant synergistic effects when coexisting. Limited mediation by depression suggests distinct biological pathways. Integrated sleep-mental health interventions may enhance cancer prevention. sleep reduction depressive symptoms cancer CHARLS cohort study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Cancer, as a global malignant disease, has become one of the leading causes of death in modern society. Characterized by complex pathogenesis, rapid progression, and poor prognosis, it poses a significant threat to health and leads to reduced quality of life [ 1 ]. Not only does cancer erode individual health, but it also imposes a substantial physical and mental burden, increasing healthcare costs and socioeconomic pressure [ 2 , 3 ]. China faces unique challenges in its cancer spectrum, where population aging and the rise of unhealthy lifestyles (including sleep problems) are expected to further increase the nation's cancer burden [ 4 ]. As the world's largest developing country, the loss of labor force, escalating medical expenditures, and the burden of family caregiving associated with cancer constitute a major public health issue [ 5 ]. Given the high global incidence and lethality of cancer, there is an urgent need to delve into its risk factors to enhance the scientific basis of prevention and treatment strategies. Sleep is a naturally recurring state of mind and body, accounting for approximately one-third of a person's lifespan. It facilitates recovery after wakefulness and ensures optimal subsequent function [ 6 ]. However, the prevalence of sleep reduction (often referred to as short sleep, e.g., ≤ 6 hours) has been increasing worldwide, and shortened sleep duration is associated with various adverse health outcomes, including risks of cardiovascular disease, diabetes, obesity, and mortality [ 7 , 8 ]. Multiple studies suggest that sleep reduction may be linked to the development of cancers such as breast cancer [ 9 ], colorectal cancer [ 10 ], as well as lung and liver cancers [ 11 ]. Depression is a mood disorder frequently overlooked in developing countries. Research indicates that depression influences multiple processes, including the immune system, endocrine system, cancer metastasis, treatment tolerance, and others [ 12 ]. Depression is more prevalent among cancer patients, affecting up to 20% of this population [ 13 ]. However, when investigating the relationship between depression and cancer, most researchers have focused on depression induced by cancer, with only a minority exploring depression as a risk factor for subsequent cancer development. Although several studies over the past decade have examined the link between depression and overall cancer risk [ 12 , 14 ], their findings have been inconsistent or require larger sample sizes for confirmation. Notably, depression often coexists with sleep problems, particularly sleep reduction [ 15 ], and the two may interact in complex ways to jointly influence cancer risk. Existing clinical observations indicate that cancer patients report shorter sleep duration more frequently than non-cancer groups, and the coexistence of both significantly elevates cancer risk [ 11 , 16 ]. This raises critical questions: Do sleep reduction and depression independently or synergistically impact cancer development? Specifically, depression, as a potential mediating variable, may amplify the carcinogenic effects of sleep reduction through biological mechanisms [ 17 , 18 ], necessitating in-depth exploration in clinical settings. Several cohort and cross-sectional studies provide preliminary evidence: retrospective analyses show that the coexistence of sleep disorders (including sleep reduction) and depression significantly increases cancer risk (adjusted odds ratio [aOR] = 6.857), and the cancer group is more prone to sleep disorders than the non-cancer group (aOR = 1.440) [ 11 ]. Large population studies (e.g., using NHANES data) further support this association, linking sleep problems (such as sleep reduction or obstructive sleep apnea) to increased cancer incidence [ 1 ]. Mechanistically, sleep reduction and depression may promote carcinogenesis by affecting various physiological processes, such as immune suppression, chronic inflammatory responses, dysregulation of endocrine and metabolic pathways, oxidative stress, and DNA damage [ 19 , 20 , 21 , 22 ]. However, most of these studies are based on data from Europe, America, or specific regions (e.g., Taiwan) and focus on diverse cancer types and populations [ 1 , 11 , 21 ]. More importantly, research specifically targeting 'sleep reduction' (as opposed to generalized sleep disorders) and its association with cancer risk in the Chinese middle-aged and elderly population, particularly in conjunction with depression, is notably lacking. Although existing evidence suggests a synergistic effect between sleep reduction and depression, to our knowledge, cohort data from a nationally representative Chinese population are still lacking. Furthermore, the role of depression in the pathway linking sleep reduction to cancer remains unclear (whether as a mediator or an independent risk factor). While some studies indicate a partial mediating effect, the results exhibit heterogeneity [ 11 , 19 , 23 , 24 ]. Sociocultural factors in China (e.g., lifestyle differences) may modify existing association conclusions. This study proposes the specific scientific question: "Among the middle-aged and elderly population in China, how does the coexistence of sleep reduction and depression independently or synergistically affect the risk of cancer morbidity?" The hypothesis is: The coexistence of sleep reduction and depression will significantly increase cancer incidence, with depression acting as a mediator in this association. The study protocol employs a cross-sectional and longitudinal cohort design, utilizing the China Health and Retirement Longitudinal Study (CHARLS) database (as a nationally representative data source) to assess the association between baseline exposures of sleep reduction and depression and subsequent cancer incidence. The aim is to quantify the hazard ratio (HR) and analyze the contribution of depression through mediation models, thereby providing high-level evidence to support cancer prevention strategies. Methods Study Population The data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS). CHARLS is a longitudinal survey of individuals aged ≥45 years in China, encompassing information on demographic characteristics, lifestyle, and health status. The baseline survey for the present study was conducted between June 2011 and March 2012. Participants were recruited using a multi-stage random sampling method across 28 provinces in China, targeting the middle-aged and elderly population (≥45 years). A total of 17,708 respondents participated at baseline. The survey was conducted every two years. All participants underwent face-to-face interviews and physical examinations performed by trained interviewers. Health status was updated through multiple follow-up waves (e.g., 2013, 2015, 2018), with the latest data available up to 2020. Descriptions of CHARLS and its questionnaires have been published elsewhere [25]. All CHARLS participants provided written informed consent, and the CHARLS project was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). The authors did not directly collect data; ownership of the data remains with the original custodians. In this study, we used the 2011 survey as the baseline. We excluded 648 participants who were aged <45 years or had missing age information. We further excluded 367 participants who self-reported (or had medical record-confirmed) a cancer diagnosis at baseline, and 1,549 participants with missing baseline core questionnaire information (including sleep duration, depression, and cancer status). Finally, we excluded 795 participants who were missing both the first follow-up after baseline and multiple subsequent follow-ups. This resulted in a final baseline analytical sample of 14,349 participants. The detailed inclusion process is described in detail in Figure 1. Measures Assessment of Sleep Duration The total sleep duration in this study was defined as the sum of self-reported nighttime sleep duration and daytime nap duration. This was assessed biennially using the following questions: “During the past month, how many hours of actual sleep did you get at night (average hours for one night)? During the past month, how long did you take a nap after lunch?”. Total sleep duration was calculated by summing self-reported nighttime sleep duration and daytime nap duration, and was recorded as a continuous variable. Based on established criteria from prior research [8], sleep duration was categorized into two groups: ≤6 hours (sleep reduction group) and >6 hours (normal sleep duration group). Assessment of Depressive Symptoms Depressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). Respondents were asked to rate “how often they felt this way during the past week.” Total scores range from 0 to 30. The CESD-10 is highly validated for use in the general population and has demonstrated adequate reliability and validity among community-dwelling older adults in China [26]. CESD-10 <10 was defined as no depressive symptoms, 10≤CESD-10＜15 was classified as mild depressive symptoms, and CESD-10 ≥15 was classified as moderate to severe depressive symptoms. Ascertainment of Cancer All participants were asked: “Have you been diagnosed with cancer or malignant tumor (excluding minor skin cancers) by a doctor?” Participants answering affirmatively were further asked: “In which organ or part of your body do you have cancer? Including the origins and metastasis of tumor: 1) Brain; 2) Oral cavity; 3) Larynx; 4) Other pharynx; 5) Thyroid; 6) Lung; 7) Breast; 8) Esophagus; 9) Stomach; 10) Liver; 11) Pancreas; 12) Kidney; 13) Prostate; 14) Testis; 15) Ovary; 16) Cervix; 17) Endometrium; 18) Colon or rectum; 19) Bladder; 20) Skin; 21) Non-Hodgkin lymphoma; 22) Leukemia; 23) Other site.” Participants answering affirmatively to the initial question were classified as having cancer. Additionally, participants who died during the study period with cancer listed as the cause of death were also identified as having cancer. Assessment of Covariates Based on prior research [21], covariates selected for this study included demographic factors (sex and age), socioeconomic factors (education and income), living conditions (marital status), behavioral habits (smoking, alcohol consumption), chronic diseases (hypertension, diabetes), and body mass index (BMI). In this study, participants were categorized into two age groups: 45–60 years and ≥60 years. Education was divided into 4 categories: primary school or below, middle school, high school, and college or above. Total household income was divided into tertiles: low-income group, middle-income group, and high-income group. Marital status was classified as married, unmarried, and other. Smoking status was categorized as smokers (previous or current smoking history) and non-smokers. Alcohol consumption was categorized as drinkers (previous or current drinking history) and non-drinkers. Hypertension was classified as present (history of hypertension) or absent. Diabetes was classified as present (history of diabetes) or absent. Body mass index (BMI) was categorized as <24 kg/m² (Normal) and ≥24 kg/m² (Overweight). Statistical Analysis Baseline characteristics were described using means±standard deviations (SD) for continuous variables and frequencies (%) for categorical variables. Continuous variables were compared between groups using Welch's t-test, while categorical variables were analyzed with chi-square tests. To examine the associations and interactions among baseline Sleep reduction, depressive symptoms, and cancer risk, we employed the Kaplan-Meier method to estimate cancer-free probability and median follow-up duration. Survival curves were visually compared using the log-rank test. Cox proportional hazards models were used to calculate hazard ratios (HRs), with multivariate regression adjustment for covariates to control confounding. The proportional hazards assumption was verified for all variables using Schoenfeld residuals, confirming model validity. These models assessed the differential effects of risk factors on cancer incidence, supplemented by subgroup analyses. Person-years of follow-up were calculated from baseline until the earliest occurrence of: incident cancer, death, loss to follow-up, or completion of the last survey wave in 2020. Kaplan-Meier survival curves were generated accordingly. Mediation analyses evaluating whether depressive symptoms mediated the association between sleep duration and cancer were performed using the "mediation" R package [27]. All statistical analyses were conducted in R software (version 4.2.2; R Foundation) and MSTATA (www.mstata.com). A two-sided P value < 0.05 was considered statistically significant. Results Baseline Characteristics The study enrolled 14349 participants, comprising 6829 (47.6%) males and 7520 (52.4%) females. The mean age was 59.1 ± 9.39 years, with 56.2% aged < 60 years. Regarding marital status, 87.8% were married, and 60.9% resided in rural areas. Educational attainment analysis revealed: 45.6% had primary school or lower education, 21.3% completed middle school, 20.8% attained high school, and 12.3% held college or higher degrees. For lifestyle factors, 67.0% were non-drinkers, 60.7% were non-smokers, and 40.3% had a body mass index (BMI) ≥ 24 (overweight). Health status indicators showed that 45.3% had hypertension, 12.2% had diabetes, and 36.8% exhibited depressive symptoms (CESD-10 ≥ 10)—categorized as mild depression (19.6%) and moderate to severe depression (17.2%). Sleep reduction (≤ 6 hours) was observed in 50.4% of participants. During follow-up (mean duration: 96.7 ± 23.88 months), 2.9% developed cancer. For detailed information, refer to Table 1 . Table 1 Patient demographics and baseline characteristics Characteristic N = 14,349 Gender, n (%) Male 6,829 (47.6%) Female 7,520 (52.4%) Age, Mean ± SD 59.1 ± 9.39 Age group, n (%) ˂ 60 8,067 (56.2%) ≥ 60 6,282 (43.8%) Marry, n (%) Married 12,602 (87.8%) Unmarried and others 1,747 (12.2%) Place of residence, n (%) Rural Village 8,734 (60.9%) Urban Community 5,615 (39.1%) Education, n (%) Primary school or below 6,533 (45.6%) Middle school 3,053 (21.3%) High school 2,983 (20.8%) College or above 1,770 (12.3%) Drinking, n (%) None 9,608 (67.0%) Yes 4,741 (33.0%) Smoking, n (%) None 8,704 (60.7%) Yes 5,644 (39.3%) BMI, n (%) ˂ 24 (Nomal) 7,270 (59.7%) ≥ 24 (Overweight) 4,905 (40.3%) Hypertension, n (%) None 7,832 (54.7%) Yes 6,484 (45.3%) Diabetes, n (%) None 12,522 (87.8%) Yes 1,736 (12.2%) Income, n (%) Low-income group 2,982 (33.3%) Middle-income group 2,982 (33.3%) High-income group 2,979 (33.3%) CESD-10, n (%) ˂ 10 (No depression) 9,064 (63.2%) 10–15 (Mild depression) 2,811 (19.6%) ≥ 15 (Moderate to severe depression) 2,474 (17.2%) Sleep duration, n (%) ≤ 6 (Sleep reduction) 7,232 (50.4%) ˃ 6 (Normal sleep) 7,117 (49.6%) Cancer at follow-up, n (%) None 13,931 (97.1%) Yes 418 (2.9%) Follow-up time, Mean ± SD(Month) 96.7 ± 23.88 Kaplan-Meier Curves and Cox Proportional Hazards Models To evaluate the association between sleep reduction and cancer risk, Kaplan-Meier cumulative risk curves based on baseline sleep duration categories demonstrated a significantly higher cancer risk in participants with sleep reduction (Fig. 2 ). Furthermore, Cox proportional hazards models were employed to investigate this relationship; as shown in Table 2 , compared to those with normal sleep duration (> 6 hours), participants with baseline sleep reduction (≤ 6 hours) exhibited a significantly increased cancer risk both in the unadjusted model (HR 1.32, 95% CI: 1.09–1.60, P = 0.005) and the model adjusted for age, sex, BMI, smoking, alcohol consumption, hypertension, diabetes, education, income, and place of residence (HR 1.33, 95% CI: 1.03–1.73, P = 0.030), indicating statistically significant associations. Table 2 The association of changes in sleep reduction with risks of incident cancer Characteristic N Event N HR 95% CI p-value Sleep duration (unadjusted) ˃ 6 (Normal sleep) 7,117 182 Reference Reference ≤ 6 (Sleep reduction) 7,232 236 1.32 1.09, 1.60 0.005 Sleep duration (adjusted)* ˃ 6 (Normal sleep) 3,628 96 Reference Reference ≤ 6 (Sleep reduction) 4,048 139 1.33 1.03, 1.73 0.030 Abbreviations: CI = Confidence Interval, HR = Hazard Ratio * adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI Using the same methodology, this study evaluated the association between depressive symptoms and cancer risk. As shown in Fig. 3 , Kaplan-Meier cumulative risk curves demonstrated significantly higher cancer risk in participants with moderate to severe depression (CESD-10 ≥ 15) compared to those without depression, whereas no significant difference was observed for mild depression (CESD-10: 10–15) versus the non-depressed group. Further quantified by Cox proportional hazards models, as detailed in Table 3 , the unadjusted model indicated a 32% increased cancer risk associated with moderate to severe depression (HR = 1.32, 95% CI: 1.03–1.69, p = 0.026), with no significant association for mild depression (HR = 1.11, 95% CI: 0.87–1.42, p = 0.412); after multivariate adjustment for age, sex, BMI, smoking, alcohol use, hypertension, diabetes, education, income, and residence, the risk for moderate to severe depression further increased to 49% and remained statistically significant (HR = 1.49, 95% CI: 1.08–2.07, p = 0.016), while mild depression still showed no significant association (HR = 1.14, 95% CI: 0.82–1.59, p = 0.425). Table 3 The association of changes in depression symptoms with risks of incident cancer Characteristic N Event N HR 95% CI p-value CESD-10 (unadjusted) ˂ 10 (No depression) 9,064 247 Reference Reference 10–15 (Mild depression) 2,811 84 1.11 0.87, 1.42 0.412 ≥ 15 (Moderate to severe depression) 2,474 87 1.32 1.03, 1.69 0.026 CESD-10 (adjusted)* ˂ 10 (No depression) 4,594 129 Reference Reference 10–15 (Mild depression) 1,608 50 1.14 0.82, 1.59 0.425 ≥ 15 (Moderate to severe depression) 1,474 56 1.49 1.08, 2.07 0.016 Abbreviations: CI = Confidence Interval, HR = Hazard Ratio * adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI Interaction and Mediation Effects Participants were categorized into four groups: 1) normal sleep duration without depressive symptoms (reference group), 2) sleep reduction only (≤ 6 hours), 3) depressive symptoms only, and 4) both sleep reduction and depressive symptoms. Kaplan-Meier analysis and Cox proportional hazards models were used to assess the joint effect of sleep reduction and depressive symptoms on cancer risk (Fig. 4 , Table 4 ). Participants with coexisting sleep reduction and depressive symptoms exhibited the highest cumulative cancer risk (log-rank test, P < 0.01). After adjusting for confounders, this group had a significantly 59% increased cancer risk (HR = 1.59, 95% CI: 1.13–2.22, P = 0.007). While the risks associated with sleep reduction only (HR = 1.18) or depressive symptoms only (HR = 1.07) were elevated, they did not reach statistical significance. To further quantify the synergistic interaction between sleep reduction and depressive symptoms on cancer risk, we calculated the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S), with 95% CIs for RERI and AP estimated using bootstrap methods. As presented in Table 5 , RERI = 0.34 (95% CI: 0.05–0.63), indicating that the cancer risk when both factors coexisted exceeded the sum of their individual effects by 34%. AP = 21.4% (95% CI: 0.03–0.39), meaning that 21.4% of the joint risk was attributable to their interaction. S = 2.36, substantially greater than 1, suggesting a significant synergistic effect. Table 4 The association of changes in sleep duration & depression symptoms with risks of incident cancer Characteristic N Event N HR 95% CI p-value Sleep duration & depression symptoms (unadjusted) Normal sleep & No depression 5,211 130 Reference Reference Sleep reduction & No depression 3,853 117 1.24 0.96, 1.59 0.093 Normal sleep & Depression 1,906 52 1.08 0.79, 1.50 0.622 Sleep reduction & Depression 3,379 119 1.47 1.15, 1.89 0.002 Sleep duration & depression symptoms (adjusted)* Normal sleep & No depression 2,529 66 Reference Reference Sleep reduction & No depression 2,065 63 1.18 0.83, 1.66 0.357 Normal sleep & Depression 1,099 30 1.07 0.69, 1.65 0.764 Sleep reduction & Depression 1,983 76 1.59 1.13, 2.22 0.007 Abbreviations: CI = Confidence Interval, HR = Hazard Ratio * adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI Table 5 Joint Interactive Effects of Sleep Reduction and Depressive Symptoms on Cancer Risk Additive Interaction Metrics Value 95%CI* Relative Excess Risk (RERI) 0.34 0.05–0.63 Attributable Proportion (AP) 21.4% 0.03–0.39 Synergy Index (S) 2.36 *95% CI excluding zero indicates statistically significant additive interaction (p < 0.05). We conducted a mediation analysis to investigate the potential mediating role of depressive symptoms in the relationship between sleep reduction and cancer. As presented in Table 6 , the total effect of sleep reduction on cancer risk was borderline significant (coefficient = 0.00753, 95% CI: 0.00033 to 0.01405, P = 0.060). The indirect effect mediated through depressive symptoms was small and non-significant (coefficient = 0.00135, 95% CI: -0.00001 to 0.00285, P = 0.080), while the direct effect of sleep reduction independent of depressive symptoms was also non-significant (coefficient = 0.00617, 95% CI: -0.00100 to 0.01314, P = 0.120). The proportion mediated by depressive symptoms was 16.5% (95% CI: -22.7 to 97.4), collectively suggesting a limited and uncertain mediating effect. Table 6 Mediation analysis for the associations between Sleep duration and Cancer at follow-up Independent variable Mediator Total effect Indirect effect Direct effect Proportion mediated, % (95% CI) Coefficient (95% CI) P value Coefficient (95% CI) P value Coefficient (95% CI) P value Sleep duration CESD-10 0.00753 (0.00033, 0.01405) 0.060 0.00135 (-0.00001, 0.00285) 0.080 0.00617 (-0.00100, 0.01314) 0.120 16.5 (-22.7, 97.4) The mediation analyses were adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI Subgroup Analysis Figure 5 illustrates the association between baseline sleep reduction and incident cancer across subgroups. Significantly stronger associations were observed in females (HR = 1.47, P = 0.026), participants aged < 60 years (HR = 1.69, P = 0.007), and those married (HR = 1.35, P = 0.034). Surprisingly, among behavioral subgroups, more pronounced effects emerged in non-smokers (HR = 1.55, P = 0.009) and non-drinkers (HR = 1.37, P = 0.046). Similarly, participants without hypertension (HR = 1.55, P = 0.009) or diabetes (HR = 1.36, P = 0.033) demonstrated stronger associations. No significant interactions were detected in other subgroups, including residence location, education level, income, or BMI (all P > 0.05). Sensitivity Analysis To assess the robustness of our findings, sensitivity analyses were conducted. First, by varying covariate sets—excluding chronic diseases (hypertension/diabetes), BMI, or unhealthy behaviors (smoking/alcohol consumption), and adjusting for sociodemographic variables only—we confirmed the stability of associations between sleep reduction, moderate to severe depressive symptoms, and cancer risk (Supplementary Tables 1–2). Results demonstrated consistent significant associations across all models, supporting the robustness. Second, multiple imputation using random forest was performed for missing covariates (e.g., income/education), with comparisons to complete-case analysis (Supplementary Tables 3–4). Third, to mitigate reverse causality, we excluded participants diagnosed with cancer during the second survey (2013) (i.e., excluding baseline potential cancer cases) and re-examined the associations (Supplementary Tables 5–6). Both the multiple imputation analyses and analyses excluding subclinical cancer cases further confirmed the reliability of the main findings. Discussion To our knowledge, this study is the first to systematically evaluate both the independent and joint effects of sleep reduction and depressive symptoms on cancer risk among middle-aged and older adults in China, using large-scale longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS). We found that sleep reduction (≤ 6 hours per day) and moderate to severe depressive symptoms (CESD-10 ≥ 15) each independently increased cancer risk—by 33% (HR = 1.33) and 49% (HR = 1.49), respectively. More importantly, when present together, they demonstrated a significant additive interaction, resulting in a 59% increase in cancer risk (RERI = 0.34, AP = 21.4%). However, mediation analysis indicated that depressive symptoms accounted for only 16.5% of the association between sleep reduction and cancer, suggesting that their synergistic effect likely operates through distinct biological pathways rather than mediation. Substantial evidence links sleep disorders to increased cancer incidence [ 28 , 29 ]. Our finding that sleep reduction (≤ 6 hours/day) significantly elevates cancer risk (HR = 1.33) aligns with international studies: NHANES reported 48% higher cancer risk with sleep disturbances [ 1 ], while Thompson et al. documented 50% increased colorectal neoplasm risk among ≤ 6-hour sleepers [ 9 ]. Mechanistically, sleep reduction promotes carcinogenesis through multiple pathways: 1) Suppressing melatonin secretion, thereby impairing its antioxidant and DNA repair functions [ 18 ]; 2) Compromising immune competence [ 21 ], as evidenced by experimental studies showing reduced natural killer (NK) cell activity and diminished tumor immune surveillance following sleep deprivation [ 30 ]; 3) Inducing chronic inflammation characterized by elevated pro-inflammatory cytokines (e.g., IL-6, TNF-α) [ 31 ], which fosters DNA damage, tumor proliferation, and metastatic potential. Notably, subgroup analyses revealed important variations in the sleep–cancer relationship. The stronger effect in females (HR = 1.47, P = 0.026) aligns with prior reports [ 32 ], potentially reflecting estrogen-sleep regulatory interactions. Higher risk among 45–60-year-olds (HR = 1.69, P = 0.007) supports Ning et al.’s hypothesis of accelerated oncogenic damage accumulation during midlife sleep deprivation [ 33 ]. Surprisingly, non-smokers (HR = 1.55) and non-drinkers (HR = 1.37) exhibited enhanced susceptibility–diverging from prior cancer-specific analyses where smoking amplified sleep-related lung cancer risk [ 34 ]. This suggests sleep reduction’s independent effects may dominate when traditional carcinogens (tobacco/alcohol) are absent. Furthermore, stronger associations in participants without hypertension (HR = 1.55) or diabetes (HR = 1.36) contradict some international studies [ 35 , 36 ], indicating heightened preventive value of sleep monitoring in chronic disease-free populations. These findings underscore the necessity of considering demographic/behavioral effect modifiers beyond reporting overall associations, particularly highlighting middle-aged Chinese women and individuals devoid of conventional risk factors as priority targets for sleep interventions. While numerous studies have linked depressive symptoms to elevated site-specific cancer risk (e.g., breast [ 37 ], lung [ 38 ], thyroid [ 39 ]), our population-based study reveals that moderate to severe depressive symptoms significantly increase overall cancer risk (HR = 1.49)—a magnitude moderately exceeding meta-analytic estimates such as Jia et al.'s weak association (RR = 1.15) [ 12 ] and van Tuijl et al.'s null finding in > 300,000 participants [ 14 ]. Potential explanations for these findings include the following: (1) Population characteristics: This study focused on middle-aged and older adults in China, where cultural factors such as stigma regarding psychological issues may contribute to underdiagnosis of depression. As a result, the association between clinically significant depressive symptoms and cancer risk may appear more pronounced in this cohort. (2) Assessment of depression: The use of the CESD-10 scale in this study, as opposed to clinical diagnoses or other instrument types in other literature, may affect the comparability of effect estimates across studies. (3) Cancer site specificity: Depression has been more consistently linked to certain cancer types, including breast and lung cancers [ 37 , 38 ], whereas the present analysis encompassed all cancer types combined, which may dilute site-specific associations. Furthermore, although mild depression (CESD-10 scores: 10–15) was not significantly associated with cancer risk in this study, there is limited evidence directly comparing graded effects of depression severity (mild, moderate, or severe) on cancer outcomes, as most prior studies treated depression as a binary or continuous variable. Thus, our findings offer new insight supporting symptom-stratified interventions for depression within cancer prevention and control efforts. A pivotal finding of this study is the 59% significantly elevated cancer risk (HR = 1.59) with coexisting sleep reduction (≤ 6h) and moderate to severe depression, demonstrating significant additive interaction (RERI = 0.34, AP = 21.4%). This aligns directionally yet differs in magnitude from Hsu et al.'s Taiwan-based study reporting 6.8-fold higher risk (aOR = 6.857) for comorbid sleep-depression disorders [ 11 ]. Methodological and demographic distinctions likely explain this discrepancy: Hsu et al.'s cross-sectional design risks reverse causality (cancer→symptoms), while their younger cohort (20–70 years) versus our middle-aged and elderly focus (≥ 45 years) dilutes relative risk estimates due to higher baseline cancer incidence in older populations. Biologically, sleep loss and depression may synergize through several mechanisms: amplified inflammatory responses [ 19 , 31 ], dysregulation of the HPA axis and cortisol secretion [ 20 ], and aggravation of health-risk behaviors [ 39 ]. Paradoxically, our mediation analysis showing depression explains only 16.5% of the sleep-cancer association suggests predominantly independent pathways rather than depression-mediated mechanisms—corroborating Lanza et al.'s "sleep-immune-cancer" direct pathway hypothesis [ 19 ]. This study helps fill an important evidence gap on the interplay between sleep, depression, and cancer in China's middle-aged and elderly population. The independent and joint effects we observe highlight the need for combined sleep and mental health interventions in cancer prevention. The modest mediation effect further suggests that future research should explore distinct biological mechanisms, possibly through inflammatory biomarkers or neuroendocrine profiles. The strengths of this study encompass: 1) nationally representative longitudinal data minimizing selection bias; 2) comprehensive multivariable adjustment for confounders coupled with interaction and mediation analyses exploring joint/independent effects; and 3) robustness verification through sensitivity analyses and multiple imputation. However, limitations include: potential measurement errors from self-reported sleep/depression metrics; exclusive focus on sleep duration without capturing other dimensions (e.g., obstructive sleep apnea, insomnia, sleep quality); unverified cancer diagnoses reliant on self-report/death records rather than medical validation; absence of site-specific cancer analyses beyond the study scope; and mediation models' inability to track depression's dynamic trajectories—necessitating future mechanistic investigations incorporating biological biomarkers (e.g., inflammatory markers). These findings suggest that sleep and mental health screening should be incorporated into routine health assessments for middle-aged and older adults. Interventions combining cognitive behavioral therapy for sleep reduction and depression with sleep hygiene education may be more effective than single-focus approaches. Public health efforts should raise awareness of sleep and mental wellbeing as modifiable cancer risk factors. Conclusion This study confirms that sleep reduction and depression (especially moderate to severe depression) are independent risk factors for cancer in middle-aged and elderly Chinese populations, exhibiting synergistic effects when coexisting. Despite depression's limited mediating role, combined sleep-depression interventions may offer novel preventive approaches. Future research must integrate biomarkers and experimental studies to elucidate their underlying mechanisms. Declarations Author Contributions: Zhenwei Jiang and Ke Hu contributed to the study conception and design. Material preparation and data collection were performed by Zhenwei Jiang, Hechun Li and Ke Hu. Formal analysis was conducted by Ke Hu and Hechun Li. The first draft of the manuscript was written by Zhenwei Jiang, and all authors commented on previous versions. Ke Hu critically reviewed and edited the manuscript. Hechun Li supervised the project administration. All authors read and approved the final manuscript. Funding: This work was supported by the Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province [Grant Number CXPJJH124009-029]. Ethics approval and consent to participate: The methods used in this study involving human participants followed the ethical guidelines laid down in the 1964 Declaration of Helsinki and its subsequent revisions. Ethical approval for all waves of the CHARLS study was obtained from the Institutional Review Board (IRB) at Peking University. The IRB approval number is IRB00001052-11015. Written signed informed consent was obtained at recruitment from all participants, including legal representatives for illiterate participants. The study exclusively enrolled adults (≥18 years), and no minors participated. Data Availability Statement: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Acknowledgments: The authors sincerely thank the China Health and Retirement Longitudinal Study (CHARLS) team for providing the data. We also extend our gratitude to all the participants and researchers who contributed to the CHARLS project. We are deeply grateful for the financial support provided by the Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province (Grant NO. CXPJJH124009-029). Additionally, we thank our colleagues from the Department of Epidemiology and Biostatistics for their valuable suggestions on statistical analysis. We are grateful for the strong support from the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen University General Hospital, and the University of Hong Kong-Shenzhen Hospital. 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Epub 2024 Apr 23. PMID: 38657774. Qiu R, Lin H, Jiang H, Shen J, He J, Fu J. Association of major depression, schizophrenia and bipolar disorder with thyroid cancer: a bidirectional two-sample mendelian randomized study. BMC Psychiatry. 2024 Apr 9;24(1):261. doi: 10.1186/s12888-024-05682-7. PMID: 38594691; PMCID: PMC11003083. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 06 Oct, 2025 Editor invited by journal 03 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 31 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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15:39:28","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":89381,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/aa21db84b563d999bda2c3e4.png"},{"id":93794712,"identity":"75ec4fa5-b118-4f21-adae-d166efdeed9f","added_by":"auto","created_at":"2025-10-17 15:39:28","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":155600,"visible":true,"origin":"","legend":"","description":"","filename":"5f395451d18643ad8eea52c6c1a54c431structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/70a4c962cc5a95660f500195.xml"},{"id":93794711,"identity":"92d66a4e-ca79-4c96-b4ca-1043c3077978","added_by":"auto","created_at":"2025-10-17 15:39:28","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":167129,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/d462b51dd0fca26b3dac35ec.html"},{"id":93797115,"identity":"8ba4435e-260d-414e-af14-cd73a981aeb4","added_by":"auto","created_at":"2025-10-17 15:55:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151666,"visible":true,"origin":"","legend":"\u003cp\u003eStudy flow chart\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/fe79b8e03d54d69eccc61ef0.png"},{"id":93794693,"identity":"2726583b-6100-44c0-9e38-8639380e24e0","added_by":"auto","created_at":"2025-10-17 15:39:28","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":214851,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier cumulative Risk of cancer based on baseline sleep duration subgroup\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/4f535728009d1ab982c92a25.jpeg"},{"id":93794696,"identity":"45fef02c-8fae-4407-a90e-3eb2e90314de","added_by":"auto","created_at":"2025-10-17 15:39:28","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119566,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier cumulative Risk of cancer based on baseline depression symptoms subgroup\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/ff8d1c519d82687d6d51635f.png"},{"id":93797116,"identity":"52dbb302-3e38-4245-8f31-c3f7b4ac4a07","added_by":"auto","created_at":"2025-10-17 15:55:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":159284,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier cumulative Risk of cancer based on baseline sleep duration \u0026amp; depression symptoms\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/9b71e451dab0ad24be048ef0.png"},{"id":93797762,"identity":"18ebe339-c5c0-46d0-9257-5801cd03f2ae","added_by":"auto","created_at":"2025-10-17 16:03:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":352821,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup Analysis for the associations between Sleep duration and Cancer at follow-up\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/8b2540132ab06f1829856d79.png"},{"id":93798531,"identity":"66f7636e-9c14-4293-bbe8-1cfa35410d89","added_by":"auto","created_at":"2025-10-17 16:11:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1945525,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/9008055a-a290-405f-92c4-73c1d8adeff5.pdf"},{"id":93795210,"identity":"8c70c3f6-1fc9-4440-8ee5-7fd2f470bbb6","added_by":"auto","created_at":"2025-10-17 15:47:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28259,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7503440/v1/761d9ed9fa1fbc66c747b0d9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sleep Reduction and Depressive Symptoms Synergistically Increase Cancer Risk in Middle-Aged and Older Chinese Adults: Evidence from the CHARLS Cohort","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer, as a global malignant disease, has become one of the leading causes of death in modern society. Characterized by complex pathogenesis, rapid progression, and poor prognosis, it poses a significant threat to health and leads to reduced quality of life [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Not only does cancer erode individual health, but it also imposes a substantial physical and mental burden, increasing healthcare costs and socioeconomic pressure [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. China faces unique challenges in its cancer spectrum, where population aging and the rise of unhealthy lifestyles (including sleep problems) are expected to further increase the nation's cancer burden [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As the world's largest developing country, the loss of labor force, escalating medical expenditures, and the burden of family caregiving associated with cancer constitute a major public health issue [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given the high global incidence and lethality of cancer, there is an urgent need to delve into its risk factors to enhance the scientific basis of prevention and treatment strategies.\u003c/p\u003e\u003cp\u003eSleep is a naturally recurring state of mind and body, accounting for approximately one-third of a person's lifespan. It facilitates recovery after wakefulness and ensures optimal subsequent function [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, the prevalence of sleep reduction (often referred to as short sleep, e.g., \u0026le;\u0026thinsp;6 hours) has been increasing worldwide, and shortened sleep duration is associated with various adverse health outcomes, including risks of cardiovascular disease, diabetes, obesity, and mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Multiple studies suggest that sleep reduction may be linked to the development of cancers such as breast cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], colorectal cancer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], as well as lung and liver cancers [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eDepression is a mood disorder frequently overlooked in developing countries. Research indicates that depression influences multiple processes, including the immune system, endocrine system, cancer metastasis, treatment tolerance, and others [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Depression is more prevalent among cancer patients, affecting up to 20% of this population [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, when investigating the relationship between depression and cancer, most researchers have focused on depression induced by cancer, with only a minority exploring depression as a risk factor for subsequent cancer development. Although several studies over the past decade have examined the link between depression and overall cancer risk [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], their findings have been inconsistent or require larger sample sizes for confirmation.\u003c/p\u003e\u003cp\u003eNotably, depression often coexists with sleep problems, particularly sleep reduction [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and the two may interact in complex ways to jointly influence cancer risk. Existing clinical observations indicate that cancer patients report shorter sleep duration more frequently than non-cancer groups, and the coexistence of both significantly elevates cancer risk [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This raises critical questions: Do sleep reduction and depression independently or synergistically impact cancer development? Specifically, depression, as a potential mediating variable, may amplify the carcinogenic effects of sleep reduction through biological mechanisms [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], necessitating in-depth exploration in clinical settings. Several cohort and cross-sectional studies provide preliminary evidence: retrospective analyses show that the coexistence of sleep disorders (including sleep reduction) and depression significantly increases cancer risk (adjusted odds ratio [aOR]\u0026thinsp;=\u0026thinsp;6.857), and the cancer group is more prone to sleep disorders than the non-cancer group (aOR\u0026thinsp;=\u0026thinsp;1.440) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Large population studies (e.g., using NHANES data) further support this association, linking sleep problems (such as sleep reduction or obstructive sleep apnea) to increased cancer incidence [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Mechanistically, sleep reduction and depression may promote carcinogenesis by affecting various physiological processes, such as immune suppression, chronic inflammatory responses, dysregulation of endocrine and metabolic pathways, oxidative stress, and DNA damage [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, most of these studies are based on data from Europe, America, or specific regions (e.g., Taiwan) and focus on diverse cancer types and populations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. More importantly, research specifically targeting 'sleep reduction' (as opposed to generalized sleep disorders) and its association with cancer risk in the Chinese middle-aged and elderly population, particularly in conjunction with depression, is notably lacking.\u003c/p\u003e\u003cp\u003eAlthough existing evidence suggests a synergistic effect between sleep reduction and depression, to our knowledge, cohort data from a nationally representative Chinese population are still lacking. Furthermore, the role of depression in the pathway linking sleep reduction to cancer remains unclear (whether as a mediator or an independent risk factor). While some studies indicate a partial mediating effect, the results exhibit heterogeneity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Sociocultural factors in China (e.g., lifestyle differences) may modify existing association conclusions. This study proposes the specific scientific question: \"Among the middle-aged and elderly population in China, how does the coexistence of sleep reduction and depression independently or synergistically affect the risk of cancer morbidity?\" The hypothesis is: The coexistence of sleep reduction and depression will significantly increase cancer incidence, with depression acting as a mediator in this association. The study protocol employs a cross-sectional and longitudinal cohort design, utilizing the China Health and Retirement Longitudinal Study (CHARLS) database (as a nationally representative data source) to assess the association between baseline exposures of sleep reduction and depression and subsequent cancer incidence. The aim is to quantify the hazard ratio (HR) and analyze the contribution of depression through mediation models, thereby providing high-level evidence to support cancer prevention strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were derived from the China Health and Retirement Longitudinal Study (CHARLS). CHARLS is a longitudinal survey of individuals aged \u0026ge;45 years in China, encompassing information on demographic characteristics, lifestyle, and health status. The baseline survey for the present study was conducted between June 2011 and March 2012. Participants were recruited using a multi-stage random sampling method across 28 provinces in China, targeting the middle-aged and elderly population (\u0026ge;45 years). A total of 17,708 respondents participated at baseline. The survey was conducted every two years. All participants underwent face-to-face interviews and physical examinations performed by trained interviewers. Health status was updated through multiple follow-up waves (e.g., 2013, 2015, 2018), with the latest data available up to 2020. Descriptions of CHARLS and its questionnaires have been published elsewhere [25]. All CHARLS participants provided written informed consent, and the CHARLS project was approved by the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015). The authors did not directly collect data; ownership of the data remains with the original custodians.\u003c/p\u003e\n\u003cp\u003eIn this study, we used the 2011 survey as the baseline. We excluded 648 participants who were aged \u0026lt;45 years or had missing age information. We further excluded 367 participants who self-reported (or had medical record-confirmed) a cancer diagnosis at baseline, and 1,549 participants with missing baseline core questionnaire information (including sleep duration, depression, and cancer status). Finally, we excluded 795 participants who were missing both the first follow-up after baseline and multiple subsequent follow-ups. This resulted in a final baseline analytical sample of 14,349 participants. The detailed inclusion process is described in detail in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Sleep Duration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe total sleep duration in this study was defined as the sum of self-reported nighttime sleep duration and daytime nap duration. This was assessed biennially using the following questions: \u0026ldquo;During the past month, how many hours of actual sleep did you get at night (average hours for one night)? During the past month, how long did you take a nap after lunch?\u0026rdquo;. Total sleep duration was calculated by summing self-reported nighttime sleep duration and daytime nap duration, and was recorded as a continuous variable. Based on established criteria from prior research [8], sleep duration was categorized into two groups: \u0026le;6 hours (sleep reduction group) and \u0026gt;6 hours (normal sleep duration group).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Depressive Symptoms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepressive symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10). Respondents were asked to rate \u0026ldquo;how often they felt this way during the past week.\u0026rdquo; Total scores range from 0 to 30. The CESD-10 is highly validated for use in the general population and has demonstrated adequate reliability and validity among community-dwelling older adults in China [26]. CESD-10 \u0026lt;10 was defined as no depressive symptoms, 10\u0026le;CESD-10＜15 was classified as mild depressive symptoms, and CESD-10 \u0026ge;15 was classified as moderate to severe depressive symptoms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAscertainment of Cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were asked: \u0026ldquo;Have you been diagnosed with cancer or malignant tumor (excluding minor skin cancers) by a doctor?\u0026rdquo; Participants answering affirmatively were further asked: \u0026ldquo;In which organ or part of your body do you have cancer? Including the origins and metastasis of tumor: 1) Brain; 2) Oral cavity; 3) Larynx; 4) Other pharynx; 5) Thyroid; 6) Lung; 7) Breast; 8) Esophagus; 9) Stomach; 10) Liver; 11) Pancreas; 12) Kidney; 13) Prostate; 14) Testis; 15) Ovary; 16) Cervix; 17) Endometrium; 18) Colon or rectum; 19) Bladder; 20) Skin; 21) Non-Hodgkin lymphoma; 22) Leukemia; 23) Other site.\u0026rdquo; Participants answering affirmatively to the initial question were classified as having cancer. Additionally, participants who died during the study period with cancer listed as the cause of death were also identified as having cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Covariates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on prior research [21], covariates selected for this study included demographic factors (sex and age), socioeconomic factors (education and income), living conditions (marital status), behavioral habits (smoking, alcohol consumption), chronic diseases (hypertension, diabetes), and body mass index (BMI). In this study, participants were categorized into two age groups: 45\u0026ndash;60 years and \u0026ge;60 years. Education was divided into 4 categories: primary school or below, middle school, high school, and college or above. Total household income was divided into tertiles: low-income group, middle-income group, and high-income group. Marital status was classified as married, unmarried, and other. Smoking status was categorized as smokers (previous or current smoking history) and non-smokers. Alcohol consumption was categorized as drinkers (previous or current drinking history) and non-drinkers. Hypertension was classified as present (history of hypertension) or absent. Diabetes was classified as present (history of diabetes) or absent. Body mass index (BMI) was categorized as \u0026lt;24 kg/m\u0026sup2; (Normal) and \u0026ge;24 kg/m\u0026sup2; (Overweight).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were described using means\u0026plusmn;standard deviations (SD) for continuous variables and frequencies (%) for categorical variables. Continuous variables were compared between groups using Welch\u0026apos;s t-test, while categorical variables were analyzed with chi-square tests.\u003c/p\u003e\n\u003cp\u003eTo examine the associations and interactions among baseline Sleep reduction, depressive symptoms, and cancer risk, we employed the Kaplan-Meier method to estimate cancer-free probability and median follow-up duration. Survival curves were visually compared using the log-rank test. Cox proportional hazards models were used to calculate hazard ratios (HRs), with multivariate regression adjustment for covariates to control confounding. The proportional hazards assumption was verified for all variables using Schoenfeld residuals, confirming model validity. These models assessed the differential effects of risk factors on cancer incidence, supplemented by subgroup analyses.\u003c/p\u003e\n\u003cp\u003ePerson-years of follow-up were calculated from baseline until the earliest occurrence of: incident cancer, death, loss to follow-up, or completion of the last survey wave in 2020. Kaplan-Meier survival curves were generated accordingly.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMediation analyses evaluating whether depressive symptoms mediated the association between sleep duration and cancer were performed using the \u0026quot;mediation\u0026quot; R package [27].\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted in R software (version 4.2.2; R Foundation) and MSTATA (www.mstata.com). A two-sided P value \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\u003cp\u003eThe study enrolled 14349 participants, comprising 6829 (47.6%) males and 7520 (52.4%) females. The mean age was 59.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39 years, with 56.2% aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years. Regarding marital status, 87.8% were married, and 60.9% resided in rural areas. Educational attainment analysis revealed: 45.6% had primary school or lower education, 21.3% completed middle school, 20.8% attained high school, and 12.3% held college or higher degrees. For lifestyle factors, 67.0% were non-drinkers, 60.7% were non-smokers, and 40.3% had a body mass index (BMI)\u0026thinsp;\u0026ge;\u0026thinsp;24 (overweight). Health status indicators showed that 45.3% had hypertension, 12.2% had diabetes, and 36.8% exhibited depressive symptoms (CESD-10\u0026thinsp;\u0026ge;\u0026thinsp;10)\u0026mdash;categorized as mild depression (19.6%) and moderate to severe depression (17.2%). Sleep reduction (\u0026le;\u0026thinsp;6 hours) was observed in 50.4% of participants. During follow-up (mean duration: 96.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.88 months), 2.9% developed cancer. For detailed information, refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient demographics and baseline characteristics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;14,349\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,829 (47.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,520 (52.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59.1\u0026thinsp;\u0026plusmn;\u0026thinsp;9.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge group, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˂ 60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,067 (56.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,282 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarry, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,602 (87.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnmarried and others\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,747 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePlace of residence, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural Village\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,734 (60.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban Community\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,615 (39.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrimary school or below\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,533 (45.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3,053 (21.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,983 (20.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCollege or above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,770 (12.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDrinking, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,608 (67.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,741 (33.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8,704 (60.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5,644 (39.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˂ 24 (Nomal)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,270 (59.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;24 (Overweight)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4,905 (40.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHypertension, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,832 (54.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e6,484 (45.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiabetes, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12,522 (87.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1,736 (12.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIncome, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-income group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,982 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle-income group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,982 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-income group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,979 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCESD-10, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˂ 10 (No depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e9,064 (63.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;15 (Mild depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,811 (19.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;15 (Moderate to severe depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2,474 (17.2%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSleep duration, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;6 (Sleep reduction)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,232 (50.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˃ 6 (Normal sleep)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7,117 (49.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCancer at follow-up, n (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13,931 (97.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e418 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFollow-up time, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD(Month)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eKaplan-Meier Curves and Cox Proportional Hazards Models\u003c/h2\u003e\u003cp\u003eTo evaluate the association between sleep reduction and cancer risk, Kaplan-Meier cumulative risk curves based on baseline sleep duration categories demonstrated a significantly higher cancer risk in participants with sleep reduction (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Furthermore, Cox proportional hazards models were employed to investigate this relationship; as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, compared to those with normal sleep duration (\u0026gt;\u0026thinsp;6 hours), participants with baseline sleep reduction (\u0026le;\u0026thinsp;6 hours) exhibited a significantly increased cancer risk both in the unadjusted model (HR 1.32, 95% CI: 1.09\u0026ndash;1.60, P\u0026thinsp;=\u0026thinsp;0.005) and the model adjusted for age, sex, BMI, smoking, alcohol consumption, hypertension, diabetes, education, income, and place of residence (HR 1.33, 95% CI: 1.03\u0026ndash;1.73, P\u0026thinsp;=\u0026thinsp;0.030), indicating statistically significant associations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association of changes in sleep reduction with risks of incident cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvent N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration (unadjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˃ 6 (Normal sleep)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e182\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;6 (Sleep reduction)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e236\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.09, 1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration (adjusted)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˃ 6 (Normal sleep)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;6 (Sleep reduction)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03, 1.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.030\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, HR\u0026thinsp;=\u0026thinsp;Hazard Ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e* adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eUsing the same methodology, this study evaluated the association between depressive symptoms and cancer risk. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Kaplan-Meier cumulative risk curves demonstrated significantly higher cancer risk in participants with moderate to severe depression (CESD-10\u0026thinsp;\u0026ge;\u0026thinsp;15) compared to those without depression, whereas no significant difference was observed for mild depression (CESD-10: 10\u0026ndash;15) versus the non-depressed group. Further quantified by Cox proportional hazards models, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the unadjusted model indicated a 32% increased cancer risk associated with moderate to severe depression (HR\u0026thinsp;=\u0026thinsp;1.32, 95% CI: 1.03\u0026ndash;1.69, p\u0026thinsp;=\u0026thinsp;0.026), with no significant association for mild depression (HR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 0.87\u0026ndash;1.42, p\u0026thinsp;=\u0026thinsp;0.412); after multivariate adjustment for age, sex, BMI, smoking, alcohol use, hypertension, diabetes, education, income, and residence, the risk for moderate to severe depression further increased to 49% and remained statistically significant (HR\u0026thinsp;=\u0026thinsp;1.49, 95% CI: 1.08\u0026ndash;2.07, p\u0026thinsp;=\u0026thinsp;0.016), while mild depression still showed no significant association (HR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 0.82\u0026ndash;1.59, p\u0026thinsp;=\u0026thinsp;0.425).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association of changes in depression symptoms with risks of incident cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvent N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCESD-10 (unadjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˂ 10 (No depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;15 (Mild depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,811\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.87, 1.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;15 (Moderate to severe depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.03, 1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.026\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCESD-10 (adjusted)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e˂ 10 (No depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u0026ndash;15 (Mild depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.82, 1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.425\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;15 (Moderate to severe depression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.08, 2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.016\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, HR\u0026thinsp;=\u0026thinsp;Hazard Ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e* adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInteraction and Mediation Effects\u003c/h2\u003e\u003cp\u003eParticipants were categorized into four groups: 1) normal sleep duration without depressive symptoms (reference group), 2) sleep reduction only (\u0026le;\u0026thinsp;6 hours), 3) depressive symptoms only, and 4) both sleep reduction and depressive symptoms. Kaplan-Meier analysis and Cox proportional hazards models were used to assess the joint effect of sleep reduction and depressive symptoms on cancer risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Participants with coexisting sleep reduction and depressive symptoms exhibited the highest cumulative cancer risk (log-rank test, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). After adjusting for confounders, this group had a significantly 59% increased cancer risk (HR\u0026thinsp;=\u0026thinsp;1.59, 95% CI: 1.13\u0026ndash;2.22, P\u0026thinsp;=\u0026thinsp;0.007). While the risks associated with sleep reduction only (HR\u0026thinsp;=\u0026thinsp;1.18) or depressive symptoms only (HR\u0026thinsp;=\u0026thinsp;1.07) were elevated, they did not reach statistical significance. To further quantify the synergistic interaction between sleep reduction and depressive symptoms on cancer risk, we calculated the relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (S), with 95% CIs for RERI and AP estimated using bootstrap methods. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, RERI\u0026thinsp;=\u0026thinsp;0.34 (95% CI: 0.05\u0026ndash;0.63), indicating that the cancer risk when both factors coexisted exceeded the sum of their individual effects by 34%. AP\u0026thinsp;=\u0026thinsp;21.4% (95% CI: 0.03\u0026ndash;0.39), meaning that 21.4% of the joint risk was attributable to their interaction. S\u0026thinsp;=\u0026thinsp;2.36, substantially greater than 1, suggesting a significant synergistic effect.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe association of changes in sleep duration \u0026amp; depression symptoms with risks of incident cancer\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEvent N\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration \u0026amp; depression symptoms\u003c/p\u003e\u003cp\u003e(unadjusted)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal sleep \u0026amp; No depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5,211\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep reduction \u0026amp; No depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,853\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.96, 1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal sleep \u0026amp; Depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.79, 1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.622\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep reduction \u0026amp; Depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3,379\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.15, 1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration \u0026amp; depression symptoms\u003c/p\u003e\u003cp\u003e(adjusted)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal sleep \u0026amp; No depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep reduction \u0026amp; No depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2,065\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.83, 1.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal sleep \u0026amp; Depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69, 1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep reduction \u0026amp; Depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.13, 2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003eAbbreviations: CI\u0026thinsp;=\u0026thinsp;Confidence Interval, HR\u0026thinsp;=\u0026thinsp;Hazard Ratio\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e* adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eJoint Interactive Effects of Sleep Reduction and Depressive Symptoms on Cancer Risk\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdditive Interaction Metrics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative Excess Risk (RERI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u0026ndash;0.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAttributable Proportion (AP)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21.4%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u0026ndash;0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSynergy Index (S)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e*95% CI excluding zero indicates statistically significant additive interaction (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWe conducted a mediation analysis to investigate the potential mediating role of depressive symptoms in the relationship between sleep reduction and cancer. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the total effect of sleep reduction on cancer risk was borderline significant (coefficient\u0026thinsp;=\u0026thinsp;0.00753, 95% CI: 0.00033 to 0.01405, P\u0026thinsp;=\u0026thinsp;0.060). The indirect effect mediated through depressive symptoms was small and non-significant (coefficient\u0026thinsp;=\u0026thinsp;0.00135, 95% CI: -0.00001 to 0.00285, P\u0026thinsp;=\u0026thinsp;0.080), while the direct effect of sleep reduction independent of depressive symptoms was also non-significant (coefficient\u0026thinsp;=\u0026thinsp;0.00617, 95% CI: -0.00100 to 0.01314, P\u0026thinsp;=\u0026thinsp;0.120). The proportion mediated by depressive symptoms was 16.5% (95% CI: -22.7 to 97.4), collectively suggesting a limited and uncertain mediating effect.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMediation analysis for the associations between Sleep duration and Cancer at follow-up\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eIndependent variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMediator\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eTotal effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eIndirect effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eDirect effect\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eProportion mediated, % (95% CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoefficient (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoefficient (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoefficient (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSleep duration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCESD-10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00753 (0.00033, 0.01405)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00135 (-0.00001, 0.00285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00617 (-0.00100, 0.01314)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16.5 (-22.7, 97.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e\u003cp\u003eThe mediation analyses were adjusted for Gender, Marry, Place of residence, Drinking, Smoking, Age, Education, Hypertension, Diabetes, Income and BMI\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eSubgroup Analysis\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the association between baseline sleep reduction and incident cancer across subgroups. Significantly stronger associations were observed in females (HR\u0026thinsp;=\u0026thinsp;1.47, P\u0026thinsp;=\u0026thinsp;0.026), participants aged\u0026thinsp;\u0026lt;\u0026thinsp;60 years (HR\u0026thinsp;=\u0026thinsp;1.69, P\u0026thinsp;=\u0026thinsp;0.007), and those married (HR\u0026thinsp;=\u0026thinsp;1.35, P\u0026thinsp;=\u0026thinsp;0.034). Surprisingly, among behavioral subgroups, more pronounced effects emerged in non-smokers (HR\u0026thinsp;=\u0026thinsp;1.55, P\u0026thinsp;=\u0026thinsp;0.009) and non-drinkers (HR\u0026thinsp;=\u0026thinsp;1.37, P\u0026thinsp;=\u0026thinsp;0.046). Similarly, participants without hypertension (HR\u0026thinsp;=\u0026thinsp;1.55, P\u0026thinsp;=\u0026thinsp;0.009) or diabetes (HR\u0026thinsp;=\u0026thinsp;1.36, P\u0026thinsp;=\u0026thinsp;0.033) demonstrated stronger associations. No significant interactions were detected in other subgroups, including residence location, education level, income, or BMI (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity Analysis\u003c/h2\u003e\u003cp\u003eTo assess the robustness of our findings, sensitivity analyses were conducted. First, by varying covariate sets\u0026mdash;excluding chronic diseases (hypertension/diabetes), BMI, or unhealthy behaviors (smoking/alcohol consumption), and adjusting for sociodemographic variables only\u0026mdash;we confirmed the stability of associations between sleep reduction, moderate to severe depressive symptoms, and cancer risk (Supplementary Tables\u0026nbsp;1\u0026ndash;2). Results demonstrated consistent significant associations across all models, supporting the robustness. Second, multiple imputation using random forest was performed for missing covariates (e.g., income/education), with comparisons to complete-case analysis (Supplementary Tables\u0026nbsp;3\u0026ndash;4). Third, to mitigate reverse causality, we excluded participants diagnosed with cancer during the second survey (2013) (i.e., excluding baseline potential cancer cases) and re-examined the associations (Supplementary Tables\u0026nbsp;5\u0026ndash;6). Both the multiple imputation analyses and analyses excluding subclinical cancer cases further confirmed the reliability of the main findings.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this study is the first to systematically evaluate both the independent and joint effects of sleep reduction and depressive symptoms on cancer risk among middle-aged and older adults in China, using large-scale longitudinal data from the China Health and Retirement Longitudinal Study (CHARLS). We found that sleep reduction (\u0026le;\u0026thinsp;6 hours per day) and moderate to severe depressive symptoms (CESD-10\u0026thinsp;\u0026ge;\u0026thinsp;15) each independently increased cancer risk\u0026mdash;by 33% (HR\u0026thinsp;=\u0026thinsp;1.33) and 49% (HR\u0026thinsp;=\u0026thinsp;1.49), respectively. More importantly, when present together, they demonstrated a significant additive interaction, resulting in a 59% increase in cancer risk (RERI\u0026thinsp;=\u0026thinsp;0.34, AP\u0026thinsp;=\u0026thinsp;21.4%). However, mediation analysis indicated that depressive symptoms accounted for only 16.5% of the association between sleep reduction and cancer, suggesting that their synergistic effect likely operates through distinct biological pathways rather than mediation.\u003c/p\u003e\u003cp\u003eSubstantial evidence links sleep disorders to increased cancer incidence [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Our finding that sleep reduction (\u0026le;\u0026thinsp;6 hours/day) significantly elevates cancer risk (HR\u0026thinsp;=\u0026thinsp;1.33) aligns with international studies: NHANES reported 48% higher cancer risk with sleep disturbances [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], while Thompson et al. documented 50% increased colorectal neoplasm risk among \u0026le;\u0026thinsp;6-hour sleepers [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Mechanistically, sleep reduction promotes carcinogenesis through multiple pathways: 1) Suppressing melatonin secretion, thereby impairing its antioxidant and DNA repair functions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]; 2) Compromising immune competence [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], as evidenced by experimental studies showing reduced natural killer (NK) cell activity and diminished tumor immune surveillance following sleep deprivation [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; 3) Inducing chronic inflammation characterized by elevated pro-inflammatory cytokines (e.g., IL-6, TNF-α) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which fosters DNA damage, tumor proliferation, and metastatic potential. Notably, subgroup analyses revealed important variations in the sleep\u0026ndash;cancer relationship. The stronger effect in females (HR\u0026thinsp;=\u0026thinsp;1.47, P\u0026thinsp;=\u0026thinsp;0.026) aligns with prior reports [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], potentially reflecting estrogen-sleep regulatory interactions. Higher risk among 45\u0026ndash;60-year-olds (HR\u0026thinsp;=\u0026thinsp;1.69, P\u0026thinsp;=\u0026thinsp;0.007) supports Ning et al.\u0026rsquo;s hypothesis of accelerated oncogenic damage accumulation during midlife sleep deprivation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Surprisingly, non-smokers (HR\u0026thinsp;=\u0026thinsp;1.55) and non-drinkers (HR\u0026thinsp;=\u0026thinsp;1.37) exhibited enhanced susceptibility\u0026ndash;diverging from prior cancer-specific analyses where smoking amplified sleep-related lung cancer risk [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This suggests sleep reduction\u0026rsquo;s independent effects may dominate when traditional carcinogens (tobacco/alcohol) are absent. Furthermore, stronger associations in participants without hypertension (HR\u0026thinsp;=\u0026thinsp;1.55) or diabetes (HR\u0026thinsp;=\u0026thinsp;1.36) contradict some international studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], indicating heightened preventive value of sleep monitoring in chronic disease-free populations. These findings underscore the necessity of considering demographic/behavioral effect modifiers beyond reporting overall associations, particularly highlighting middle-aged Chinese women and individuals devoid of conventional risk factors as priority targets for sleep interventions.\u003c/p\u003e\u003cp\u003eWhile numerous studies have linked depressive symptoms to elevated site-specific cancer risk (e.g., breast [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], lung [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], thyroid [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]), our population-based study reveals that moderate to severe depressive symptoms significantly increase overall cancer risk (HR\u0026thinsp;=\u0026thinsp;1.49)\u0026mdash;a magnitude moderately exceeding meta-analytic estimates such as Jia et al.'s weak association (RR\u0026thinsp;=\u0026thinsp;1.15) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and van Tuijl et al.'s null finding in \u0026gt;\u0026thinsp;300,000 participants [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Potential explanations for these findings include the following: (1) Population characteristics: This study focused on middle-aged and older adults in China, where cultural factors such as stigma regarding psychological issues may contribute to underdiagnosis of depression. As a result, the association between clinically significant depressive symptoms and cancer risk may appear more pronounced in this cohort. (2) Assessment of depression: The use of the CESD-10 scale in this study, as opposed to clinical diagnoses or other instrument types in other literature, may affect the comparability of effect estimates across studies. (3) Cancer site specificity: Depression has been more consistently linked to certain cancer types, including breast and lung cancers [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], whereas the present analysis encompassed all cancer types combined, which may dilute site-specific associations. Furthermore, although mild depression (CESD-10 scores: 10\u0026ndash;15) was not significantly associated with cancer risk in this study, there is limited evidence directly comparing graded effects of depression severity (mild, moderate, or severe) on cancer outcomes, as most prior studies treated depression as a binary or continuous variable. Thus, our findings offer new insight supporting symptom-stratified interventions for depression within cancer prevention and control efforts.\u003c/p\u003e\u003cp\u003eA pivotal finding of this study is the 59% significantly elevated cancer risk (HR\u0026thinsp;=\u0026thinsp;1.59) with coexisting sleep reduction (\u0026le;\u0026thinsp;6h) and moderate to severe depression, demonstrating significant additive interaction (RERI\u0026thinsp;=\u0026thinsp;0.34, AP\u0026thinsp;=\u0026thinsp;21.4%). This aligns directionally yet differs in magnitude from Hsu et al.'s Taiwan-based study reporting 6.8-fold higher risk (aOR\u0026thinsp;=\u0026thinsp;6.857) for comorbid sleep-depression disorders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Methodological and demographic distinctions likely explain this discrepancy: Hsu et al.'s cross-sectional design risks reverse causality (cancer\u0026rarr;symptoms), while their younger cohort (20\u0026ndash;70 years) versus our middle-aged and elderly focus (\u0026ge;\u0026thinsp;45 years) dilutes relative risk estimates due to higher baseline cancer incidence in older populations. Biologically, sleep loss and depression may synergize through several mechanisms: amplified inflammatory responses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], dysregulation of the HPA axis and cortisol secretion [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and aggravation of health-risk behaviors [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Paradoxically, our mediation analysis showing depression explains only 16.5% of the sleep-cancer association suggests predominantly independent pathways rather than depression-mediated mechanisms\u0026mdash;corroborating Lanza et al.'s \"sleep-immune-cancer\" direct pathway hypothesis [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study helps fill an important evidence gap on the interplay between sleep, depression, and cancer in China's middle-aged and elderly population. The independent and joint effects we observe highlight the need for combined sleep and mental health interventions in cancer prevention. The modest mediation effect further suggests that future research should explore distinct biological mechanisms, possibly through inflammatory biomarkers or neuroendocrine profiles.\u003c/p\u003e\u003cp\u003eThe strengths of this study encompass: 1) nationally representative longitudinal data minimizing selection bias; 2) comprehensive multivariable adjustment for confounders coupled with interaction and mediation analyses exploring joint/independent effects; and 3) robustness verification through sensitivity analyses and multiple imputation. However, limitations include: potential measurement errors from self-reported sleep/depression metrics; exclusive focus on sleep duration without capturing other dimensions (e.g., obstructive sleep apnea, insomnia, sleep quality); unverified cancer diagnoses reliant on self-report/death records rather than medical validation; absence of site-specific cancer analyses beyond the study scope; and mediation models' inability to track depression's dynamic trajectories\u0026mdash;necessitating future mechanistic investigations incorporating biological biomarkers (e.g., inflammatory markers).\u003c/p\u003e\u003cp\u003eThese findings suggest that sleep and mental health screening should be incorporated into routine health assessments for middle-aged and older adults. Interventions combining cognitive behavioral therapy for sleep reduction and depression with sleep hygiene education may be more effective than single-focus approaches. Public health efforts should raise awareness of sleep and mental wellbeing as modifiable cancer risk factors.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study confirms that sleep reduction and depression (especially moderate to severe depression) are independent risk factors for cancer in middle-aged and elderly Chinese populations, exhibiting synergistic effects when coexisting. Despite depression's limited mediating role, combined sleep-depression interventions may offer novel preventive approaches. Future research must integrate biomarkers and experimental studies to elucidate their underlying mechanisms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eZhenwei Jiang and Ke Hu contributed to the study conception and design. Material preparation and data collection were performed by Zhenwei Jiang, Hechun Li and Ke Hu. Formal analysis was conducted by Ke Hu and Hechun Li. The first draft of the manuscript was written by Zhenwei Jiang, and all authors commented on previous versions. Ke Hu critically reviewed and edited the manuscript. Hechun Li supervised the project administration. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province [Grant Number CXPJJH124009-029].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u0026nbsp;\u003c/strong\u003eThe methods used in this study involving human participants followed the ethical guidelines laid down in the 1964 Declaration of Helsinki and its subsequent revisions. Ethical approval for all waves of the CHARLS study was obtained from the Institutional Review Board (IRB) at Peking University. The IRB approval number is IRB00001052-11015. Written signed informed consent was obtained at recruitment from all participants, including legal representatives for illiterate participants. The study exclusively enrolled adults (\u0026ge;18 years), and no minors participated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors sincerely thank the China Health and Retirement Longitudinal Study (CHARLS) team for providing the data. We also extend our gratitude to all the participants and researchers who contributed to the CHARLS project. We are deeply grateful for the financial support provided by the Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province (Grant NO. CXPJJH124009-029). Additionally, we thank our colleagues from the Department of Epidemiology and Biostatistics for their valuable suggestions on statistical analysis. We are grateful for the strong support from the Seventh Affiliated Hospital of Sun Yat-sen University, Shenzhen University General Hospital, and the University of Hong Kong-Shenzhen Hospital. Finally, we appreciate the anonymous reviewers and editors for their constructive feedback, which significantly improved the quality of this manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003ePorcacchia AS, Pires GN, Andersen ML, Tufik S. A cross-sectional analysis of the association between sleep disorders and cancer using data from the National Health and Nutrition Examination Survey (NHANES) 2005-2014. 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PMID: 38594691; PMCID: PMC11003083.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sleep reduction, depressive symptoms, cancer, CHARLS, cohort study","lastPublishedDoi":"10.21203/rs.3.rs-7503440/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7503440/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe independent and joint effects of sleep reduction and depression on cancer risk remain understudied in Chinese populations, particularly regarding synergistic mechanisms. This study aims to examine the impact of sleep reduction (\u0026le;\u0026thinsp;6 hours) and depressive symptoms on cancer incidence among middle-aged and older adults in China. We will analyze both the independent and combined effects of these two factors, in order to provide important insights for understanding cancer etiology and informing prevention strategies.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe study utilized robust longitudinal data (2011\u0026ndash;2020) from 14,349 cancer-free adults aged\u0026thinsp;\u0026ge;\u0026thinsp;45 years in the China Health and Retirement Longitudinal Study (CHARLS). We meticulously examined associations between baseline sleep duration (\u0026le;\u0026thinsp;6h vs. \u0026gt;6h), depressive symptoms (CESD- 10\u0026thinsp;\u0026lt;\u0026thinsp;10 was defined as no depressive symptoms, 10\u0026thinsp;\u0026le;\u0026thinsp;CESD- 10 \u0026lt;15 was classified as mild depressive symptoms, and CESD-10\u0026thinsp;\u0026ge;\u0026thinsp;15 was classified as moderate to severe depressive symptoms.), and incident cancer. Our Cox models quantified hazard ratios (HRs) adjusted for sociodemographic, behavioural, and clinical confounders. We also performed additive interaction (RERI/AP/S) and mediation analyses, ensuring a comprehensive understanding of the data.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOver 96.7\u0026thinsp;\u0026plusmn;\u0026thinsp;23.88 months, 418 cancer cases occurred. Sleep reduction (\u0026le;\u0026thinsp;6h) independently increased cancer risk by 33% (adjusted HR\u0026thinsp;=\u0026thinsp;1.33, 95% CI:1.03\u0026ndash;1.73), while moderate-severe depression increased risk by 49% (HR\u0026thinsp;=\u0026thinsp;1.49, 1.08\u0026ndash;2.07). When these two factors coexisted, they demonstrated synergistic effects, meaning their combined impact was greater than the sum of their individual effects: the combined risk was 59% higher (HR\u0026thinsp;=\u0026thinsp;1.59, 1.13\u0026ndash;2.22) with significant additive interaction (RERI\u0026thinsp;=\u0026thinsp;0.34, 95% CI:0.05\u0026ndash;0.63; AP\u0026thinsp;=\u0026thinsp;21.4%, 0.03\u0026ndash;0.39). Depression mediated only 16.5% (95% CI: -22.7-97.4) of the sleep-cancer association. Subgroups at highest risk included females, adults\u0026thinsp;\u0026lt;\u0026thinsp;60 years, and those without hypertension/diabetes.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eSleep reduction and depression (especially moderate to severe depression) independently increase cancer risk in middle-aged/older Chinese adults, with significant synergistic effects when coexisting. Limited mediation by depression suggests distinct biological pathways. Integrated sleep-mental health interventions may enhance cancer prevention.\u003c/p\u003e","manuscriptTitle":"Sleep Reduction and Depressive Symptoms Synergistically Increase Cancer Risk in Middle-Aged and Older Chinese Adults: Evidence from the CHARLS Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 15:39:23","doi":"10.21203/rs.3.rs-7503440/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-10-06T10:54:41+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-03T10:44:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T06:30:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T06:27:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-09-01T02:04:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"648d18c3-ea3d-45d1-a50f-afc6a81b7e3a","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-17T15:39:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 15:39:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7503440","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7503440","identity":"rs-7503440","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}