Short sleep duration as a risk factor for cataract in the elderly hypertensive patients in rural China

Short sleep duration as a risk factor for cataract in the elderly hypertensive patients in rural China | 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 Short sleep duration as a risk factor for cataract in the elderly hypertensive patients in rural China Dongbin Yang, Chen Li, Mingze Ma, Yunhui Xue, Xinghong Guo, Shiyu Jia, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4954564/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In an aging society, cataracts continue to significantly impact the quality of life for an increasing number of elderly individuals. As a risk factor for cataract, hypertension is becoming increasingly prevalent among the elderly year by year. The association between sleep duration and cataract in elderly hypertensive demographic remains unclear and warrants further exploration to aid in strategizing early intervention programs. Methods Based on China’s National Basic Public Health Service Project (NBPHSP), a cross-sectional study was conducted in Jia County, Henan Province, China. A total of 17473 cases aged 65 years and over with hypertension were included in this study. Sleep duration was obtained through questionnaires and information on cataracts was derived from NBPHSP. Three logistic regression models were used to assess the association between sleep duration and cataract. Subgroup analysis and interaction tests were performed to address heterogeneity. Results The average self-reported sleep duration was (6.77 ± 1.80) hours, and the prevalence of cataracts was 11.9%. In the adjusted logistic regression model, elderly hypertensive patients with sleep duration < 6 hours had a higher risk of cataract compared to those with sleep duration between 7–8 hours (OR: 1.39, 95%CI: 1.21–1.59). However, non-significant association was found between long sleep duration and cataract. The findings from subgroup analysis indicated no significant interaction effect. Conclusions In rural China, elderly hypertensive patients with a sleep duration of less than 6 hours are at a significantly higher risk of developing cataracts. This finding underscores the importance of monitoring sleep patterns in this population. Promoting adequate sleep duration may be a key strategy in reducing cataract prevalence and improving the overall quality of life for elderly patients with hypertension. Sleep duration Cataract Elderly Hypertension China Figures Figure 1 Figure 2 Introduction Cataracts are the leading cause of visual impairment globally, emerging as a significant public health issue that demands attention. According to data released by The Lancet Global Health, cataracts have rendered blindness for over 15 million adults aged 50 years and above worldwide [ 1 ] . In China, the age-standardized disability-adjusted life years rate due to cataracts was 66.84 (95%UI 47.08–90.43) for women and 49.06 (95%UI 34.4–67.10) for men in 2019 [ 2 ] . Although vision loss from cataracts can be restored through surgery, many patients do not receive timely treatment due to various barriers, including inadequate awareness about the disease, limited medical resources, and financial constraints which in turn contribute to low cataract surgical coverage [ 3 ] . In 2020, the cataract surgical coverage rate of the elderly in rural areas of southern China was reported to be 40.6% [ 4 ] . Therefore, early identification of modifiable risk factors is particularly important for preventing cataracts and reducing the associated health and economic burdens. Hypertension is a systemic disease characterized by elevated blood pressure, which is one of the main causes of cardiovascular diseases and death globally [ 5 ] . Numerous studies have found that hypertension can elevate the risk of cataract [ 6 , 7 ] . In China, the rapidly aging population has led to over half of the elderly suffering from hypertension, with prevalence rates consistently rising, particularly in rural areas [ 8 , 9 ] . These trends imply that in the near future, the prevalence of cataracts among the elderly population will continue to rise, severely threatening the health and quality of life and increasing the public health burden. Both sleep deprivation and excessive sleep have been linked to various diseases and health conditions [ 10 – 13 ] . The National Sleep Foundation of the United States recommends 7–8 hours of sleep for people over the age of 65 [ 14 ] . Taking this as the standard, a cohort study found that the proportion of Chinese elderly over 65 years old with insufficient or excessive sleep duration was close to 70%, reflecting the current sleep dilemma faced by Chinese elderly [ 15 ] . Although an increasing body of evidence suggests a correlation between sleep duration and cataract, the findings remain inconsistent and the conclusions are still debated. For example, a national community health survey in South Korea found that sleep deprivation may increase the risk of cataract in adults whereas a study of rural South African people aged 40 years and older found a non-significant association between sleep duration and cataract after adjustment for potential confounders [ 16 , 17 ] . Therefore, the link between sleep duration and cataract, especially in the elderly, needs to be further explored. However, there has not been a large-scale study on this specific population. To address the current gap in relevant studies, this research conducted a large-scale cross-sectional survey involving 18,963 elderly hypertensive patients in rural China. The aim was to explore the relationship between sleep duration and the prevalence of cataracts, thereby providing epidemiological evidence to inform prevention and pathogenesis for cataracts in elderly hypertensive patients. Methods Study design and participants From July 1, 2023 to August 31, 2023, a cross-sectional study was conducted in Jia County, Henan Province, China. A total of 18,963 elderly hypertensive patients aged 65 years and above were recruited using cluster sampling method. The data on history of hypertension were obtained from the physical examination data of the elderly under China’s National Basic Public Health Service Project (NBPHSP). The exclusion criteria were as follows: inability to complete the survey due to hearing impairment, severe cognitive dysfunction, incomplete basic information, and missing relevant lifestyle information. Ultimately, 17,473 participants met these criteria and were included in the analysis, as illustrated in Fig. 1 . This study was approved by the Life Science Ethics Review Committee of Zhengzhou University (registration number: 2023 − 318). All the participants signed the informed consent form after they were fully aware of the purpose of the study. If they could not sign it, their family members signed it on their behalf. Here is Fig. 1 Figure 1 Flowchart of data processing and analysis. Sleep duration During the face-to-face interview, the Pittsburgh Sleep Quality Index [ 18 ] was used to gather the sleep-related information from the respondents. Baseline sleep time was obtained from participants through self-reporting. In the questionnaire, participants were asked "In the past month, how many hours of actual sleep did you get per night (the average number of hours per night)?". According to a previous study [ 19 ] , sleep duration was classified as < 6 hours, 6–7 hours, 7–8 hours, and ≥ 8 hours per day. Cataract The cataracts involved in this study cover all types, such as senile cataract, congenital cataract, complicated cataract, and traumatic cataract. Information on the prevalence of cataracts in elderly hypertensive patients was sourced from the NBPHSP and includes only those who voluntarily visited the hospital and were diagnosed by doctors. It should be noted that older adults who had previously undergone cataract surgery and recovered from cataracts were also included in the survey. Covariates Referring to previous studies [ 20 – 22 ] , participants completed a structured questionnaire to provide baseline information on demographic characteristics (age, gender, marital status, education, and occupation), lifestyle factors (history of cigarette smoking, history of alcohol drinking, and physical activity level), and the prevalence of chronic diseases (obesity, diabetes). Considering the close relationship between solar radiation exposure of outdoor workers and the occurrence of cataracts [ 23 ] , most of the participants in this study were farmers, who were exposed to solar radiation for a long time during outdoor work, so the occupation was divided by whether they were farmers or not. Physical activity level was measured using the International Physical Activity Questionnaire Short Form. Height, weight, and waist circumference were measured twice for all participants according to a standard protocol and were analyzed with the use of the mean values. With reference to the Chinese standard, in addition to general obesity (body mass index ≥ 28 kg / m 2 ), combined with the research in recent years, we also consider the central obesity (waist circumference ≥ 90 cm for men, waist circumference ≥ 85 cm for women). The diagnosis information of diabetes was obtained from NBPHSP. Statistical analysis SPSS (version 25.0) and R software (version 4.3.2) were used for statistical analysis. The characteristics of the participants were presented as the mean ± standard deviation (SD) for continuous variables and as the frequency (proportion) for categorical variables. Differences in prevalence of cataracts between groups were compared by analysis of variance for continuous variables and by chi-square tests for categorical variables. Three logistic regression models, both with and without adjusting for potential confounders, were used to explore the independent relationship between sleep duration and cataract. Model 1 was not adjusted for any covariates while Model 2 was adjusted for age, gender, marital status, education, occupation and history of cigarette smoking. Model 3 was adjusted for age, gender, marital status, education, occupation, history of cigarette smoking, central obesity and diabetes. Considering the potential for different biological mechanisms in factors such as gender, we conducted a subgroup analysis to test the reliability of the results. Differences were regarded as statistically significant if P values were less than 0.05. Results Participant Characteristics A total of 17473 elderly patients with hypertension were included in this survey, with an average age of (73.45 ± 5.77) years. Among the 17473 participants, 7376 (52.3%) were males. The average sleep duration was (6.77 ± 1.80) hours, and the sleep duration < 6 hours, 6–7 hours (including 6 hours), 7–8 hours (including 7 hours), and ≥ 8 hours accounted for 23.6%, 19.0%, 22.1%, and 35.3%, respectively. In addition, the number of cataract patients was 2076, and the prevalence rate was 11.9%. The results are presented in Table 1 . Table 1 Characteristics of Elderly hypertensive patients Characteristics Total Non-cataract Cataract P -value (n = 17473) (n = 15397) (n = 2076) Age (years) 73.5 ± 5.8 73.3 ± 5.8 74.4 ± 5.7 < 0.001 Gender, n (%) < 0.001 Male 7376 (52.3) 6725 (43.7) 651 (31.4) Female 10097 (47.7) 8672 (56.3) 1425 (68.6) Marital status, n (%) < 0.001 Married 12485 (71.5) 11094 (72.1) 1391 (67.0) Others 4988 (28.5) 4303 (27.9) 685 (33.0) Education, n (%) < 0.001 Elementary school or below 13790 (78.9) 12083 (78.5) 1707 (82.2) Middle school 2855 (16.3) 2553 (16.6) 302 (14.5) High school or above 828 (4.7) 761 (4.9) 67 (3.2) Occupation, n (%) 0.043 Farmer 11498 (65.8) 10173 (66.1) 1325 (63.8) Others 5975 (34.2) 5224 (33.9) 751 (36.2) Physical activity level, n (%) 0.187 Low 2925 (16.7) 2604 (16.9) 321 (15.5) Middle 5262 (30.1) 4612 (30.0) 650 (31.3) High 9286 (53.1) 8181 (53.1) 1105 (53.2) History of cigarette smoking, n (%) < 0.001 No 13185 (75.5) 11550 (75.0) 1635 (78.8) Yes 4288 (24.5) 3847 (25.0) 441 (21.2) History of alcohol drinking, n (%) 0.077 No 15610 (89.3) 13732 (89.2) 1878 (90.5) Yes 1863 (10.7) 1665 (10.8) 198 (9.5) General obesity, n (%) 0.457 No 14435 (82.6) 12732 (82.7) 1703 (82.0) Yes 3038 (17.4) 2665 (17.3) 373 (18.0) Central obesity, n (%) No 5408 (31.0) 4857 (31.5) 551 (26.5) < 0.001 Yes 12065 (69.0) 10540 (68.5) 1525 (73.5) Diabetes, n (%) No 13209 (75.6) 11915 (77.4) 1294 (62.3) < 0.001 Yes 4264 (24.4) 3482 (22.6) 782 (37.7) Sleep duration, n (%) < 0.001 < 6h 4124 (23.6) 3513 (22.8) 611 (29.4) 6-7h 3322 (19.0) 2959 (19.2) 363 (17.5) 7-8h 3861 (22.1) 3445 (22.4) 416 (20.0) ≥ 8h 6166 (35.3) 5480 (35.6) 686 (33.0) Table 1 Characteristics of Elderly hypertensive patients Here is Table 1 Logistic regression analysis between sleep duration with cataract The results of the logistic regression model used to examine the relationship between sleep duration and cataract are presented in Table 2 . In the fully adjusted model (model 3), elderly hypertensive patients who slept less than 6 hours had a higher risk of cataract than those who slept between 7 and 8 hours (OR: 1.39, 95% CI: 1.21–1.59). The results of the unadjusted model (model 1) and the partially adjusted model (model 2) are similar to those of model 3. The fully adjusted logistic regression model is shown in Fig. 2 . The results show that in addition to sleep duration, older age, female, history of cigarette smoking and diabetes are the risk factors for cataract in elderly hypertensive patients. Table 2 Logistic regression analysis between sleep duration with cataract Sleep duration Model1 a Model2 b Model3 c OR (95%CI) P -value OR (95%CI) P -value OR (95%CI) P -value 7-8h Ref Ref Ref < 6h 1.44 (1.26–1.65) < 0.001 1.37 (1.20–1.57) < 0.001 1.39 (1.21–1.59) < 0.001 6-7h 1.02 (0.88–1.18) 0.836 0.99 (0.85–1.15) 0.891 1.00 (0.89–1.16) 0.978 ≥ 8h 1.04 (0.91–1.18) 0.584 1.03 (0.90–1.17) 0.663 1.02 (0.90–1.16) 0.768 a Adjusted for no covariates; b Adjusted for age, gender, marital status, education, occupation and history of cigarette smoking; c Adjusted for age, gender, marital status, education, occupation, history of cigarette smoking, central obesity and diabetes. Table 2 Logistic regression analysis between sleep duration with cataract Here is Table 2 Here is Fig. 2 Figure 2 Fully adjusted logistic regression model Subgroup analysis of sleep duration and cataract Subgroup analyses were performed to check the reliability of the results for each group and the detailed results are presented in Table 3 . the correlation between the risk of depression and < 6 h of sleep duration was consistent in all subgroups. After adjusting for all relevant risk factors, no significant interaction effects were observed in subgroup analyses, and all interaction effect P values exceeded 0.05. The association between cataract risk and sleep duration less than 6 hours was consistent in all subgroups Table 3 subgroup analyses of the correlation between sleep duration and cataract Subgroups 7-8h < 6h 6-7h ≥ 8h P for interaction OR (95%CI) OR (95%CI) OR (95%CI) OR (95%CI) Age 0.191 65–69 Ref 1.47 (1.12–1.94) 1.12 (0.83–1.52) 1.01 (0.77–1.33) 70–74 Ref 1.48 (1.17–1.87) 1.09 (0.84–1.41) 1.03 (0.82–1.31) 75–79 Ref 1.11 (0.85–1.43) 0.85 (0.63–1.13) 0.81 (0.63–1.03) ≥ 80 Ref 1.58 (1.12–2.24) 0.88 (0.59–1.32) 1.36 (0.99–1.87) Gender 0.773 Male Ref 1.45 (1.14–1.85) 0.93 (0.71–1.22) 1.04 (0.83–1.29) Female Ref 1.34 (1.14–1.58) 1.01 (0.84–1.21) 0.99 (0.84–1.17) Marital status 0.628 Married Ref 1.46 (1.24–1.73) 1.03 (0.86–1.24) 1.06 (0.90–1.24) Others Ref 1.21 (0.95–1.53) 0.91 (0.70–1.19) 0.92 (0.73–1.16) Education 0.647 Elementary school or below Ref 1.35 (1.17–1.57) 1.01 (0.85–1.19) 0.97 (0.84–1.12) Middle school Ref 1.50 (1.04–2.16) 0.95 (0.63–1.44) 1.21 (0.86–1.69) High school or above Ref 1.36 (0.61-3.00) 0.61 (0.25–1.45) 1.01 (0.51–1.97) Occupation 0.057 Farmer Ref 1.43 (1.14–1.79) 0.98 (0.76–1.26) 0.85 (0.69–1.06) Others Ref 1.36 (1.15–1.61) 0.99 (0.82–1.20) 1.12 (0.95–1.32) History of cigarette smoking 0.891 No Ref 1.36 (1.17–1.59) 1.00 (0.84–1.18) 1.03 (0.88–1.19) Yes Ref 1.44 (1.07–1.93) 0.96 (0.70–1.33) 0.97 (0.73–1.27) Central obesity 0.673 No Ref 1.49 (1.14–1.94) 1.02 (0.76–1.38) 1.15 (0.89–1.47) Yes Ref 1.34 (1.15–1.57) 0.98 (0.82–1.17) 0.97 (0.83–1.13) Diabetes 0.982 No Ref 1.36 (1.15–1.61) 0.98 (0.82–1.19) 1.02 (0.86–1.20) Yes Ref 1.41 (1.12–1.77) 1.01 (0.78–1.30) 1.00 (0.81–1.25) Table 3 subgroup analyses of the correlation between sleep duration and cataract Here is Table 3 Discussion In this cross-sectional survey of 18,963 rural elderly hypertensive patients aged 65 years and older, those who slept less than 6 hours had a higher risk of cataract compared with those who slept between 7 and 8 hours. This observation persisted even after adjustment for demographic characteristics, socioeconomic status, and prevalence of metabolic diseases, including diabetes and central obesity. In the fully adjusted regression model, elderly hypertensive patients who slept less than 6 hours had an approximately 39% increased risk of cataract compared with those who slept between 7 and 8 hours, which was similar to the results of a previous study [ 17 ] . In addition, no significant association was found between sleep duration between 6 and 7 hours or sleep duration more than 8 hours and the risk of cataract. The prevalence of cataracts in elderly patients with hypertension was 11.9%. However, a previous large cross-sectional study reported that the prevalence of cataracts in the elderly population was only 5.01% [ 24 ] , which provided a certain degree of evidence support for the view that hypertension is a potential risk factor for cataract. Hypertension has a profound effect on the structure and function of the eye, and the most widely known hypertensive eye disease is hypertensive retinopathy, which involves damage to the retina [ 25 , 26 ] . In recent years, more and more laboratory evidence supported that hypertension is a risk factor for cataract. A large number of studies have shown that the renin-angiotensin-aldosterone system regulates blood pressure, activates the immune system to trigger inflammation and produce a large number of reactive oxygen species (ROS) [ 27 ] . When the production of ROS exceeds the antioxidant capacity of cells, it will further lead to oxidative stress [ 28 , 29 ] . The oxidation process will lead to the destabilization of lens proteins, which will promote the accumulation of proteins and eventually lead to lens opacity and cause cataracts [ 30 ] . Therefore, elderly hypertensive patients are more likely to develop cataracts. Studies have shown that insufficient sleep may cause eye diseases such as myopia [ 31 ] , glaucoma [ 32 ] , age-related macular degeneration [ 33 ] and dry eye [ 34 ] , which seriously affect patients' daily life. Similarly, sleep duration is also strongly associated with cataract, a major cause of visual impairment in older adults [ 35 ] . Our study demonstrated that insufficient sleep is a risk factor for cataract, aligning with the findings of several previous studies [ 16 , 36 ] . For example, a cross-sectional study of adults aged 50 years and older in the United States assessed sleep health based on sleep duration, with sleep duration ≥ 10 hours or < 6 hours defined as poor sleep health, and showed a negative correlation between sleep health and the risk of cataract [ 36 ] . In a Korean study of 715,554 adults aged 40 years and older, those who slept less than 6 hours per day were more likely to develop cataracts compared with those who slept more than 9 hours per day (OR = 1.22, 99%CI, 1.11–1.34) [ 16 ] . A meta-analysis based on cross-sectional surveys showed that sleep deprivation significantly increases the risk of cataract (OR: 1.20, 95% CI: 1.05–1.36) [ 37 ] . In this study, the study population was 65 years old and above, so we took the sleep duration (7–8 hours) recommended by the National Sleep Foundation of the United States as the reference group [ 14 ] . The results showed that, whether in the unadjusted, partially adjusted or fully adjusted models, elderly patients with hypertension who slept less than 6 hours had a higher risk of cataract compared with those who slept 7–8 hours. Individuals with insufficient sleep are likely to be exposed to light for a longer time at night, which can inhibit the secretion of melatonin to a certain extent [ 38 ] . It is worth mentioning that melatonin is an important antioxidant for the lens [ 39 ] . Therefore, it is speculated that melatonin may protect lens cells and delay the occurrence of cataract by fighting ROS and decrease oxidative stress [ 40 ] . In addition, laboratory and epidemiological studies suggest that lack of sleep may contribute to an increased incidence of diabetes by promoting appetite [ 41 ] . In diabetic patients, excess glucose is converted into sorbitol and accumulates in large amounts in lens cells, resulting in damage and liquefaction of lens fibers, and eventually the formation of cataract [ 42 ] . The primary strength of this study lies in its focus on elderly patients with hypertension aged 65 and above, set against the backdrop of global aging and the rising number of elderly hypertensive individuals. This focus ensures that the study results are highly targeted and relevant. In addition, the large and sufficiently representative sample size, along with the conducted subgroup analyses ensures our findings are generalizable to a broader population. However, several limitations of this study should also be considered. First of all, this is a cross-sectional study, which cannot determine the causal relationship between sleep duration and cataract in elderly hypertensive patients. Further longitudinal and interventional studies are warranted to clarify the causal inference and underlying mechanisms. Second, in our current study, sleep duration was self-reported, which may have introduced some information bias. Third, although our analyses included various potential confounders and associated clinical characteristics, we could not rule out the possibility that unmeasured confounders may have confounded our results, such as the possible effect of dietary factors on the risk of cataract. Finally, the prevalence of cataracts in this study was all confirmed by patients who went to the hospital or underwent physical examination due to significant vision loss or even blindness. Therefore, compared with the actual situation, the prevalence of cataracts in this study will be lower. Conclusions In summary, among elderly patients with hypertension in rural China, those who slept for less than 6 hours were at a higher risk of developing cataracts compared to those who slept for 7–8 hours. In addition, age, gender, history of cigarette smoking, diabetes are also the important factors influencing the risk of elderly hypertensive patients with cataracts. Therefore, In the process of prevention and treatment of cataract in rural areas, we should pay more attention to the sleep status of elderly patients with hypertension, encourage adequate night rest and promote good lifestyle to delay the occurrence of cataracts. Abbreviations UI Uncertainty Interval CI Confidence Interval OR Odds Ratio NBPHSP National Basic Public Health Service Project ROS Reactive Oxygen Species. Declarations Ethics approval and consent to participate This study was approved by the Life Science Ethics Review Committee of Zhengzhou University (registration number: 2023-318) and informed consent was obtained from all participants. Consent for publication Not applicable. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Grants from the Platform for Dynamic Monitoring and Comprehensive Evaluation of Healthy Central Plains Action (20220134B); Zhengzhou University Education Reform Research and Practice Project (2023ZZUJGXM222); Henan Zhongyuan Medical Science and Technology Innovation and Development Foundation (23YCG1006). Authors’ contributions BY, YM and DY contributed to the conceptualization; MM, BY, CL, and XG contributed to the methodology; BY and DY contributed to the formal analysis; JW, YM, YX, LZ, and MM contributed to the investigation; YM, CL, YX, and SJ contributed to the data curation; ND, RL, and QZ contributed to the funding acquisition; JW and YM contributed to the project administration; CL contributed to writing—original draft; DY, CST, and BY contributed to writing—review and editing; Acknowledgements We express our gratitude to all researchers and participants involved in this cross-sectional study. 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Association of sleep behaviour and pattern with the risk of glaucoma: a prospective cohort study in the UK Biobank. BMJ Open. 2022;12(11):e063676. Lei S, Liu Z, Li H. Sleep duration and age-related macular degeneration: a cross-sectional and Mendelian randomization study. Front Aging Neurosci. 2023;15:1247413. Li S, Ning K, Zhou J, Guo Y, Zhang H, Zhu Y, et al. Sleep deprivation disrupts the lacrimal system and induces dry eye disease. Exp Mol Med. 2018;50(3):e451. Gao Y, Liu J, Zhou W, Tian J, Wang Q, Zhou L. Exploring factors influencing visual disability in the elderly population of China: A nested case-control investigation. J Glob Health. 2023;13:04142. Meng Y, Tan Z, Sawut A, Li L, Chen C. Association between Life's Essential 8 and cataract among US adults. Sci Rep. 2024;14(1):13101. Zhou M, Li D, Kai J, Zhang X, Pan C. Sleep duration and the risk of major eye disorders: a systematic review and meta-analysis. Eye (Lond). 2023;37(13):2707-15. McIntyre IM, Norman TR, Burrows GD, Armstrong SM. Human melatonin suppression by light is intensity dependent. J Pineal Res. 1989;6(2):149-56. Ahmad SB, Ali A, Bilal M, Rashid SM, Wani AB, Bhat RR, et al. Melatonin and Health: Insights of Melatonin Action, Biological Functions, and Associated Disorders. Cell Mol Neurobiol. 2023;43(6):2437-58. Rong X, Rao J, Li D, Jing Q, Lu Y, Ji Y. TRIM69 inhibits cataractogenesis by negatively regulating p53. Redox Biol. 2019;22:101157. Knutson KL, Spiegel K, Penev P, Van Cauter E. The metabolic consequences of sleep deprivation. Sleep Med Rev. 2007;11(3):163-78. Sayin N, Kara N, Pekel G. Ocular complications of diabetes mellitus. World J Diabetes. 2015;6(1):92-108. Additional Declarations No competing interests reported. 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Li","email":"","orcid":"","institution":"Department of Health management, College of Public Health, Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Li","suffix":""},{"id":344015212,"identity":"d2df2dd4-515c-4d58-aaff-0d55f56687ae","order_by":2,"name":"Mingze Ma","email":"","orcid":"","institution":"Department of Health management, College of Public Health, Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingze","middleName":"","lastName":"Ma","suffix":""},{"id":344015213,"identity":"ba2671cb-f1e0-45f8-9255-d9df2db9ee36","order_by":3,"name":"Yunhui Xue","email":"","orcid":"","institution":"Department of Health management, College of Public Health, Zhengzhou 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University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qiuping","middleName":"","lastName":"Zhao","suffix":""},{"id":344015221,"identity":"eded8f5f-e852-4ae6-b00a-cb80e961f5e4","order_by":11,"name":"Yudong Miao","email":"","orcid":"","institution":"Department of Health management, College of Public Health, Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yudong","middleName":"","lastName":"Miao","suffix":""},{"id":344015222,"identity":"1c932c29-9c43-4b53-9485-b87b0fa0bd81","order_by":12,"name":"Jian Wu","email":"","orcid":"","institution":"Department of Health management, College of Public Health, Zhengzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wu","suffix":""},{"id":344015223,"identity":"f227e272-1b42-4195-9871-577c43023f9a","order_by":13,"name":"Beizhu Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIie3LoQvCQBTH8TcGsxxa34Lon3AgqOA/8w5Bi5gNhgNhTVb9MwTBfONgK6fWBUEtJoM2g4gDi+mczXBfeOHB7wPgcv1lDIBAQe39+T+QUP5EoCBclSU8myfH42MvVrvsjDDpCVnZKDsx2z4ndhZrpQYIZigkG5Od5KM2EmqxTmSKXqSFRMbt5HDp3IlrsZp5EXrPMiRnbSDSYhn4AXqyBAnNuI+kdGthAr9L6bAVsZGdVLNNcrs/dD2Os1N+nfbqccXYSVN9flRcYN0XNeS3hcvlcrleil1LdzSmyHgAAAAASUVORK5CYII=","orcid":"","institution":"Department of Health management, College of Public Health, Zhengzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Beizhu","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2024-08-22 02:33:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4954564/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4954564/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66674957,"identity":"4e16c474-5ab6-452a-b7a3-d5512b8ffcf9","added_by":"auto","created_at":"2024-10-15 11:03:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":42264,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of data processing and analysis.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4954564/v1/737584dfae5dfba591907dcd.jpg"},{"id":66674958,"identity":"0e7cb0d2-3764-4387-a4fe-8577250a3446","added_by":"auto","created_at":"2024-10-15 11:03:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56573,"visible":true,"origin":"","legend":"\u003cp\u003eFully adjusted logistic regression model\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4954564/v1/52c8ab25cd070a0ec5a22cbe.jpg"},{"id":85342682,"identity":"b3165522-d10c-4ade-a8a8-8f93ac2fd210","added_by":"auto","created_at":"2025-06-25 00:46:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1152194,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4954564/v1/940076f3-52a0-42a5-82e7-56e43954fe31.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short sleep duration as a risk factor for cataract in the elderly hypertensive patients in rural China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCataracts are the leading cause of visual impairment globally, emerging as a significant public health issue that demands attention. According to data released by The Lancet Global Health, cataracts have rendered blindness for over 15\u0026nbsp;million adults aged 50 years and above worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. In China, the age-standardized disability-adjusted life years rate due to cataracts was 66.84 (95%UI 47.08\u0026ndash;90.43) for women and 49.06 (95%UI 34.4\u0026ndash;67.10) for men in 2019\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Although vision loss from cataracts can be restored through surgery, many patients do not receive timely treatment due to various barriers, including inadequate awareness about the disease, limited medical resources, and financial constraints which in turn contribute to low cataract surgical coverage\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In 2020, the cataract surgical coverage rate of the elderly in rural areas of southern China was reported to be 40.6%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Therefore, early identification of modifiable risk factors is particularly important for preventing cataracts and reducing the associated health and economic burdens.\u003c/p\u003e \u003cp\u003eHypertension is a systemic disease characterized by elevated blood pressure, which is one of the main causes of cardiovascular diseases and death globally\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have found that hypertension can elevate the risk of cataract\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. In China, the rapidly aging population has led to over half of the elderly suffering from hypertension, with prevalence rates consistently rising, particularly in rural areas\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. These trends imply that in the near future, the prevalence of cataracts among the elderly population will continue to rise, severely threatening the health and quality of life and increasing the public health burden.\u003c/p\u003e \u003cp\u003eBoth sleep deprivation and excessive sleep have been linked to various diseases and health conditions\u003csup\u003e[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. The National Sleep Foundation of the United States recommends 7\u0026ndash;8 hours of sleep for people over the age of 65\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Taking this as the standard, a cohort study found that the proportion of Chinese elderly over 65 years old with insufficient or excessive sleep duration was close to 70%, reflecting the current sleep dilemma faced by Chinese elderly\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Although an increasing body of evidence suggests a correlation between sleep duration and cataract, the findings remain inconsistent and the conclusions are still debated. For example, a national community health survey in South Korea found that sleep deprivation may increase the risk of cataract in adults whereas a study of rural South African people aged 40 years and older found a non-significant association between sleep duration and cataract after adjustment for potential confounders\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Therefore, the link between sleep duration and cataract, especially in the elderly, needs to be further explored.\u003c/p\u003e \u003cp\u003eHowever, there has not been a large-scale study on this specific population. To address the current gap in relevant studies, this research conducted a large-scale cross-sectional survey involving 18,963 elderly hypertensive patients in rural China. The aim was to explore the relationship between sleep duration and the prevalence of cataracts, thereby providing epidemiological evidence to inform prevention and pathogenesis for cataracts in elderly hypertensive patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eFrom July 1, 2023 to August 31, 2023, a cross-sectional study was conducted in Jia County, Henan Province, China. A total of 18,963 elderly hypertensive patients aged 65 years and above were recruited using cluster sampling method. The data on history of hypertension were obtained from the physical examination data of the elderly under China\u0026rsquo;s National Basic Public Health Service Project (NBPHSP). The exclusion criteria were as follows: inability to complete the survey due to hearing impairment, severe cognitive dysfunction, incomplete basic information, and missing relevant lifestyle information. Ultimately, 17,473 participants met these criteria and were included in the analysis, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This study was approved by the Life Science Ethics Review Committee of Zhengzhou University (registration number: 2023\u0026thinsp;\u0026minus;\u0026thinsp;318). All the participants signed the informed consent form after they were fully aware of the purpose of the study. If they could not sign it, their family members signed it on their behalf.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eHere is Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Flowchart of data processing and analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSleep duration\u003c/h2\u003e \u003cp\u003eDuring the face-to-face interview, the Pittsburgh Sleep Quality Index\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e was used to gather the sleep-related information from the respondents. Baseline sleep time was obtained from participants through self-reporting. In the questionnaire, participants were asked \"In the past month, how many hours of actual sleep did you get per night (the average number of hours per night)?\". According to a previous study\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, sleep duration was classified as \u0026lt;\u0026thinsp;6 hours, 6\u0026ndash;7 hours, 7\u0026ndash;8 hours, and \u0026ge;\u0026thinsp;8 hours per day.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCataract\u003c/h2\u003e \u003cp\u003eThe cataracts involved in this study cover all types, such as senile cataract, congenital cataract, complicated cataract, and traumatic cataract. Information on the prevalence of cataracts in elderly hypertensive patients was sourced from the NBPHSP and includes only those who voluntarily visited the hospital and were diagnosed by doctors. It should be noted that older adults who had previously undergone cataract surgery and recovered from cataracts were also included in the survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eCovariates\u003c/h2\u003e \u003cp\u003eReferring to previous studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, participants completed a structured questionnaire to provide baseline information on demographic characteristics (age, gender, marital status, education, and occupation), lifestyle factors (history of cigarette smoking, history of alcohol drinking, and physical activity level), and the prevalence of chronic diseases (obesity, diabetes). Considering the close relationship between solar radiation exposure of outdoor workers and the occurrence of cataracts\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e, most of the participants in this study were farmers, who were exposed to solar radiation for a long time during outdoor work, so the occupation was divided by whether they were farmers or not. Physical activity level was measured using the International Physical Activity Questionnaire Short Form. Height, weight, and waist circumference were measured twice for all participants according to a standard protocol and were analyzed with the use of the mean values. With reference to the Chinese standard, in addition to general obesity (body mass index\u0026thinsp;\u0026ge;\u0026thinsp;28 kg / m\u003csup\u003e2\u003c/sup\u003e), combined with the research in recent years, we also consider the central obesity (waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm for men, waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;85 cm for women). The diagnosis information of diabetes was obtained from NBPHSP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eSPSS (version 25.0) and R software (version 4.3.2) were used for statistical analysis. The characteristics of the participants were presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variables and as the frequency (proportion) for categorical variables. Differences in prevalence of cataracts between groups were compared by analysis of variance for continuous variables and by chi-square tests for categorical variables. Three logistic regression models, both with and without adjusting for potential confounders, were used to explore the independent relationship between sleep duration and cataract. Model 1 was not adjusted for any covariates while Model 2 was adjusted for age, gender, marital status, education, occupation and history of cigarette smoking. Model 3 was adjusted for age, gender, marital status, education, occupation, history of cigarette smoking, central obesity and diabetes. Considering the potential for different biological mechanisms in factors such as gender, we conducted a subgroup analysis to test the reliability of the results. Differences were regarded as statistically significant if \u003cem\u003eP\u003c/em\u003e values were less than 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 17473 elderly patients with hypertension were included in this survey, with an average age of (73.45\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77) years. Among the 17473 participants, 7376 (52.3%) were males. The average sleep duration was (6.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80) hours, and the sleep duration\u0026thinsp;\u0026lt;\u0026thinsp;6 hours, 6\u0026ndash;7 hours (including 6 hours), 7\u0026ndash;8 hours (including 7 hours), and \u0026ge;\u0026thinsp;8 hours accounted for 23.6%, 19.0%, 22.1%, and 35.3%, respectively. In addition, the number of cataract patients was 2076, and the prevalence rate was 11.9%. The results are presented in 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\u003eCharacteristics of Elderly hypertensive patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-cataract\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCataract\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17473)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;15397)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2076)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.3\u0026thinsp;\u0026plusmn;\u0026thinsp;5.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7376 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6725 (43.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e651 (31.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10097 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8672 (56.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1425 (68.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12485 (71.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11094 (72.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1391 (67.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4988 (28.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4303 (27.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e685 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13790 (78.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12083 (78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1707 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e2855 (16.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2553 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302 (14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e828 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e761 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11498 (65.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10173 (66.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1325 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5975 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5224 (33.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e751 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity level, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2925 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2604 (16.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5262 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4612 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e650 (31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9286 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8181 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1105 (53.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of cigarette smoking, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13185 (75.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11550 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1635 (78.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4288 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3847 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e441 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory of alcohol drinking, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15610 (89.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13732 (89.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1878 (90.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1863 (10.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1665 (10.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198 (9.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral obesity, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14435 (82.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12732 (82.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1703 (82.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3038 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2665 (17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e373 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral obesity, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5408 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4857 (31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e551 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e12065 (69.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10540 (68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1525 (73.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13209 (75.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11915 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1294 (62.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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=\"left\" colname=\"c2\"\u003e \u003cp\u003e4264 (24.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3482 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e782 (37.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4124 (23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3513 (22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e611 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3322 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2959 (19.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e363 (17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3861 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3445 (22.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e416 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6166 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5480 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e686 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e Characteristics of Elderly hypertensive patients\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eHere is Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eLogistic regression analysis between sleep duration with cataract\u003c/h2\u003e \u003cp\u003eThe results of the logistic regression model used to examine the relationship between sleep duration and cataract are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. In the fully adjusted model (model 3), elderly hypertensive patients who slept less than 6 hours had a higher risk of cataract than those who slept between 7 and 8 hours (OR: 1.39, 95% CI: 1.21\u0026ndash;1.59). The results of the unadjusted model (model 1) and the partially adjusted model (model 2) are similar to those of model 3. The fully adjusted logistic regression model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results show that in addition to sleep duration, older age, female, history of cigarette smoking and diabetes are the risk factors for cataract in elderly hypertensive patients.\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\u003eLogistic regression analysis between sleep duration with cataract\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel2\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel3\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \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 \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.44 (1.26\u0026ndash;1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.37 (1.20\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39 (1.21\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6-7h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02 (0.88\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.85\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (0.89\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8h\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.04 (0.91\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.90\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02 (0.90\u0026ndash;1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted for no covariates;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003eb\u003c/sup\u003eAdjusted for age, gender, marital status, education, occupation and history of cigarette smoking;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003csup\u003ec\u003c/sup\u003eAdjusted for age, gender, marital status, education, occupation, history of cigarette smoking, central obesity and diabetes.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Logistic regression analysis between sleep duration with cataract\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eHere is Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eHere is Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e Fully adjusted logistic regression model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis of sleep duration and cataract\u003c/h2\u003e \u003cp\u003eSubgroup analyses were performed to check the reliability of the results for each group and the detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. the correlation between the risk of depression and \u0026lt;\u0026thinsp;6 h of sleep duration was consistent in all subgroups. After adjusting for all relevant risk factors, no significant interaction effects were observed in subgroup analyses, and all interaction effect \u003cem\u003eP\u003c/em\u003e values exceeded 0.05. The association between cataract risk and sleep duration less than 6 hours was consistent in all subgroups\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\u003esubgroup analyses of the correlation between sleep duration and cataract\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7-8h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6-7h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8h\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.47 (1.12\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.12 (0.83\u0026ndash;1.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.77\u0026ndash;1.33)\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\u003e70\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48 (1.17\u0026ndash;1.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.09 (0.84\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.82\u0026ndash;1.31)\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\u003e75\u0026ndash;79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11 (0.85\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85 (0.63\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.81 (0.63\u0026ndash;1.03)\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\u0026ge;\u0026thinsp;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.58 (1.12\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.88 (0.59\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.36 (0.99\u0026ndash;1.87)\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\u003eGender\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.773\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.45 (1.14\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.93 (0.71\u0026ndash;1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04 (0.83\u0026ndash;1.29)\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.14\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.84\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.84\u0026ndash;1.17)\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\u003eMarital status\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.46 (1.24\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.86\u0026ndash;1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.06 (0.90\u0026ndash;1.24)\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21 (0.95\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91 (0.70\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92 (0.73\u0026ndash;1.16)\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\u003eEducation\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElementary school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.35 (1.17\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.85\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.84\u0026ndash;1.12)\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\u003eMiddle school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.50 (1.04\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95 (0.63\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.21 (0.86\u0026ndash;1.69)\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\u003eHigh school or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36 (0.61-3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.61 (0.25\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (0.51\u0026ndash;1.97)\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\u003eOccupation\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.43 (1.14\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.76\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.85 (0.69\u0026ndash;1.06)\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\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.15\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.82\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.12 (0.95\u0026ndash;1.32)\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\u003eHistory of cigarette smoking\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.17\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.84\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03 (0.88\u0026ndash;1.19)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.44 (1.07\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.70\u0026ndash;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.73\u0026ndash;1.27)\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\u003eCentral obesity\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.49 (1.14\u0026ndash;1.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02 (0.76\u0026ndash;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15 (0.89\u0026ndash;1.47)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.15\u0026ndash;1.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.82\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.97 (0.83\u0026ndash;1.13)\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\u003eDiabetes\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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.15\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.82\u0026ndash;1.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.02 (0.86\u0026ndash;1.20)\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\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41 (1.12\u0026ndash;1.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (0.78\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.81\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e subgroup analyses of the correlation between sleep duration and cataract\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHere is Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/h2\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this cross-sectional survey of 18,963 rural elderly hypertensive patients aged 65 years and older, those who slept less than 6 hours had a higher risk of cataract compared with those who slept between 7 and 8 hours. This observation persisted even after adjustment for demographic characteristics, socioeconomic status, and prevalence of metabolic diseases, including diabetes and central obesity. In the fully adjusted regression model, elderly hypertensive patients who slept less than 6 hours had an approximately 39% increased risk of cataract compared with those who slept between 7 and 8 hours, which was similar to the results of a previous study\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. In addition, no significant association was found between sleep duration between 6 and 7 hours or sleep duration more than 8 hours and the risk of cataract.\u003c/p\u003e \u003cp\u003eThe prevalence of cataracts in elderly patients with hypertension was 11.9%. However, a previous large cross-sectional study reported that the prevalence of cataracts in the elderly population was only 5.01%\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e, which provided a certain degree of evidence support for the view that hypertension is a potential risk factor for cataract. Hypertension has a profound effect on the structure and function of the eye, and the most widely known hypertensive eye disease is hypertensive retinopathy, which involves damage to the retina\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In recent years, more and more laboratory evidence supported that hypertension is a risk factor for cataract. A large number of studies have shown that the renin-angiotensin-aldosterone system regulates blood pressure, activates the immune system to trigger inflammation and produce a large number of reactive oxygen species (ROS)\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. When the production of ROS exceeds the antioxidant capacity of cells, it will further lead to oxidative stress\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The oxidation process will lead to the destabilization of lens proteins, which will promote the accumulation of proteins and eventually lead to lens opacity and cause cataracts\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Therefore, elderly hypertensive patients are more likely to develop cataracts.\u003c/p\u003e \u003cp\u003eStudies have shown that insufficient sleep may cause eye diseases such as myopia\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, glaucoma\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, age-related macular degeneration\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e and dry eye\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, which seriously affect patients' daily life. Similarly, sleep duration is also strongly associated with cataract, a major cause of visual impairment in older adults\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Our study demonstrated that insufficient sleep is a risk factor for cataract, aligning with the findings of several previous studies\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. For example, a cross-sectional study of adults aged 50 years and older in the United States assessed sleep health based on sleep duration, with sleep duration\u0026thinsp;\u0026ge;\u0026thinsp;10 hours or \u0026lt;\u0026thinsp;6 hours defined as poor sleep health, and showed a negative correlation between sleep health and the risk of cataract\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. In a Korean study of 715,554 adults aged 40 years and older, those who slept less than 6 hours per day were more likely to develop cataracts compared with those who slept more than 9 hours per day (OR\u0026thinsp;=\u0026thinsp;1.22, 99%CI, 1.11\u0026ndash;1.34)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. A meta-analysis based on cross-sectional surveys showed that sleep deprivation significantly increases the risk of cataract (OR: 1.20, 95% CI: 1.05\u0026ndash;1.36)\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. In this study, the study population was 65 years old and above, so we took the sleep duration (7\u0026ndash;8 hours) recommended by the National Sleep Foundation of the United States as the reference group\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The results showed that, whether in the unadjusted, partially adjusted or fully adjusted models, elderly patients with hypertension who slept less than 6 hours had a higher risk of cataract compared with those who slept 7\u0026ndash;8 hours. Individuals with insufficient sleep are likely to be exposed to light for a longer time at night, which can inhibit the secretion of melatonin to a certain extent\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. It is worth mentioning that melatonin is an important antioxidant for the lens\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is speculated that melatonin may protect lens cells and delay the occurrence of cataract by fighting ROS and decrease oxidative stress\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. In addition, laboratory and epidemiological studies suggest that lack of sleep may contribute to an increased incidence of diabetes by promoting appetite\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. In diabetic patients, excess glucose is converted into sorbitol and accumulates in large amounts in lens cells, resulting in damage and liquefaction of lens fibers, and eventually the formation of cataract\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe primary strength of this study lies in its focus on elderly patients with hypertension aged 65 and above, set against the backdrop of global aging and the rising number of elderly hypertensive individuals. This focus ensures that the study results are highly targeted and relevant. In addition, the large and sufficiently representative sample size, along with the conducted subgroup analyses ensures our findings are generalizable to a broader population. However, several limitations of this study should also be considered. First of all, this is a cross-sectional study, which cannot determine the causal relationship between sleep duration and cataract in elderly hypertensive patients. Further longitudinal and interventional studies are warranted to clarify the causal inference and underlying mechanisms. Second, in our current study, sleep duration was self-reported, which may have introduced some information bias. Third, although our analyses included various potential confounders and associated clinical characteristics, we could not rule out the possibility that unmeasured confounders may have confounded our results, such as the possible effect of dietary factors on the risk of cataract. Finally, the prevalence of cataracts in this study was all confirmed by patients who went to the hospital or underwent physical examination due to significant vision loss or even blindness. Therefore, compared with the actual situation, the prevalence of cataracts in this study will be lower.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, among elderly patients with hypertension in rural China, those who slept for less than 6 hours were at a higher risk of developing cataracts compared to those who slept for 7\u0026ndash;8 hours. In addition, age, gender, history of cigarette smoking, diabetes are also the important factors influencing the risk of elderly hypertensive patients with cataracts. Therefore, In the process of prevention and treatment of cataract in rural areas, we should pay more attention to the sleep status of elderly patients with hypertension, encourage adequate night rest and promote good lifestyle to delay the occurrence of cataracts.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUncertainty Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdds Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNBPHSP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Basic Public Health Service Project\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive Oxygen Species.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Life Science Ethics Review Committee of Zhengzhou University (registration number: 2023-318) and informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Grants from the Platform for Dynamic Monitoring and Comprehensive Evaluation of Healthy Central Plains Action (20220134B); Zhengzhou University Education Reform Research and Practice Project (2023ZZUJGXM222); Henan Zhongyuan Medical Science and Technology Innovation and Development Foundation (23YCG1006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBY, YM and DY contributed to the conceptualization; MM, BY, CL, and XG contributed to the methodology; BY and DY contributed to the formal analysis; JW, YM, YX, LZ, and MM contributed to the investigation; YM, CL, YX, and SJ contributed to the data curation; ND, RL, and QZ contributed to the funding acquisition; JW and YM contributed to the project administration; CL contributed to writing\u0026mdash;original draft; DY, CST, and BY contributed to writing\u0026mdash;review and editing;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to all researchers and participants involved in this cross-sectional study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2019 Blindness and Vision Impairment Collaborators \u0026amp; Vision Loss Expert Group of the Global Burden of Disease Study. 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Asia-Pac J Ophthalmol. 2023;12(6):565-73.\u003c/li\u003e\n\u003cli\u003eMartin SS, Aday AW, Almarzooq ZI, Anderson CAM, Arora P, Avery CL, et al. 2024 Heart Disease and Stroke Statistics: A Report of US and Global Data From the American Heart Association. Circulation. 2024;149(8):e347-913.\u003c/li\u003e\n\u003cli\u003eMylona I, Dermenoudi M, Ziakas N, Tsinopoulos I. Hypertension is the Prominent Risk Factor in Cataract Patients. Medicina (Kaunas). 2019;55(8):430.\u003c/li\u003e\n\u003cli\u003eSabanayagam C, Wang JJ, Mitchell P, Tan AG, Tai ES, Aung T, et al. Metabolic Syndrome Components and Age-Related Cataract: The Singapore Malay Eye Study. Investigative Ophthalmology \u0026amp; Visual Science. 2011;52(5):2397-404.\u003c/li\u003e\n\u003cli\u003eGuo R, Xie Y, Zheng J, Dai Y, Wang Y, Zheng L. Analysis of hypertension in the Chinese elderly population with hypertension. Chinese Journal of Geriatrics. 2020;39(5):591-4.\u003c/li\u003e\n\u003cli\u003eLu J, Lu Y, Wang X. 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National Sleep Foundation\u0026apos;s updated sleep duration recommendations: final report. Sleep Health. 2015;1(4):233-43.\u003c/li\u003e\n\u003cli\u003eLi W, Kondracki A, Gautam P, Rahman A, Kiplagat S, Liu H, et al. The association between sleep duration, napping, and stroke stratified by self-health status among Chinese people over 65 years old from the China health and retirement longitudinal study. Sleep Breath. 2021;25(3):1239-46.\u003c/li\u003e\n\u003cli\u003eRim TH, Kim DW, Kim SE, Kim SS. Factors Associated with Cataract in Korea: A Community Health Survey 2008-2012. Yonsei Med J. 2015;56(6):1663-70.\u003c/li\u003e\n\u003cli\u003ePeltzer K, Pengpid S. Self-Reported Sleep Duration and Its Correlates with Sociodemographics, Health Behaviours, Poor Mental Health, and Chronic Conditions in Rural Persons 40 Years and Older in South Africa. Int J Environ Res Public Health. 2018;15(7):1357.\u003c/li\u003e\n\u003cli\u003eBuysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 1989;28(2):193-213.\u003c/li\u003e\n\u003cli\u003eKobayashi D, Kuriyama N, Osugi Y, Arioka H, Takahashi O. Longitudinal relationships between cardiovascular events, risk factors, and time-dependent sleep duration. Cardiol J. 2018;25(2):229-35.\u003c/li\u003e\n\u003cli\u003eModenese A, Korpinen L, Gobba F. Solar Radiation Exposure and Outdoor Work: An Underestimated Occupational Risk. Int J Environ Res Public Health. 2018;15(10):2063.\u003c/li\u003e\n\u003cli\u003eNam SW, Lim DH, Cho KY, Kim HS, Kim K, Chung TY. Risk factors of presenile nuclear cataract in health screening study. BMC Ophthalmol. 2018;18(1):263.\u003c/li\u003e\n\u003cli\u003eDastgheib SA, Rezaianzadeh A, Maharlouei N, Rahimikazerooni S, Lankarani KB. Gender difference in determinant factors of being overweight among the 40-70-year-old population of Kharameh cohort study, Iran. BMC Public Health. 2021;21(1):746.\u003c/li\u003e\n\u003cli\u003eModenese A, Gobba F. Cataract frequency and subtypes involved in workers assessed for their solar radiation exposure: a systematic review. Acta Ophthalmol. 2018;96(8):779-88.\u003c/li\u003e\n\u003cli\u003eYuan M, Han Y, Fang Y, Chu C-I. Childbearing May Increase the Risk of Nondiabetic Cataract in Chinese Women\u0026rsquo;s Old Age. Journal of Ophthalmology. 2015;2015(1):385815.\u003c/li\u003e\n\u003cli\u003eCheung CY, Biousse V, Keane PA, Schiffrin EL, Wong TY. Hypertensive eye disease. Nat Rev Dis Primers. 2022;8(1):14.\u003c/li\u003e\n\u003cli\u003eWong TY, Mitchell P. The eye in hypertension. Lancet. 2007;369(9559):425-35.\u003c/li\u003e\n\u003cli\u003eAmponsah-Offeh M, Diaba-Nuhoho P, Speier S, Morawietz H. Oxidative Stress, Antioxidants and Hypertension. Antioxidants (Basel). 2023;12(2):281.\u003c/li\u003e\n\u003cli\u003eSenoner T, Dichtl W. Oxidative Stress in Cardiovascular Diseases: Still a Therapeutic Target? Nutrients. 2019;11(9):2090.\u003c/li\u003e\n\u003cli\u003eGhezzi P, Jaquet V, Marcucci F, Schmidt H. The oxidative stress theory of disease: levels of evidence and epistemological aspects. Br J Pharmacol. 2017;174(12):1784-96.\u003c/li\u003e\n\u003cli\u003eTruscott RJW, Friedrich MG. Molecular Processes Implicated in Human Age-Related Nuclear Cataract. Invest Ophthalmol Vis Sci. 2019;60(15):5007-21.\u003c/li\u003e\n\u003cli\u003eWang X, Liu X, Lin Q, Dong P, Wei Y, Liu J. Association between sleep duration, sleep quality, bedtime and myopia: A systematic review and meta-analysis. Clin Exp Ophthalmol. 2023;51(7):673-84.\u003c/li\u003e\n\u003cli\u003eSun C, Yang H, Hu Y, Qu Y, Hu Y, Sun Y, et al. Association of sleep behaviour and pattern with the risk of glaucoma: a prospective cohort study in the UK Biobank. BMJ Open. 2022;12(11):e063676.\u003c/li\u003e\n\u003cli\u003eLei S, Liu Z, Li H. Sleep duration and age-related macular degeneration: a cross-sectional and Mendelian randomization study. Front Aging Neurosci. 2023;15:1247413.\u003c/li\u003e\n\u003cli\u003eLi S, Ning K, Zhou J, Guo Y, Zhang H, Zhu Y, et al. Sleep deprivation disrupts the lacrimal system and induces dry eye disease. Exp Mol Med. 2018;50(3):e451.\u003c/li\u003e\n\u003cli\u003eGao Y, Liu J, Zhou W, Tian J, Wang Q, Zhou L. Exploring factors influencing visual disability in the elderly population of China: A nested case-control investigation. J Glob Health. 2023;13:04142.\u003c/li\u003e\n\u003cli\u003eMeng Y, Tan Z, Sawut A, Li L, Chen C. Association between Life\u0026apos;s Essential 8 and cataract among US adults. Sci Rep. 2024;14(1):13101.\u003c/li\u003e\n\u003cli\u003eZhou M, Li D, Kai J, Zhang X, Pan C. Sleep duration and the risk of major eye disorders: a systematic review and meta-analysis. Eye (Lond). 2023;37(13):2707-15.\u003c/li\u003e\n\u003cli\u003eMcIntyre IM, Norman TR, Burrows GD, Armstrong SM. Human melatonin suppression by light is intensity dependent. J Pineal Res. 1989;6(2):149-56.\u003c/li\u003e\n\u003cli\u003eAhmad SB, Ali A, Bilal M, Rashid SM, Wani AB, Bhat RR, et al. Melatonin and Health: Insights of Melatonin Action, Biological Functions, and Associated Disorders. Cell Mol Neurobiol. 2023;43(6):2437-58.\u003c/li\u003e\n\u003cli\u003eRong X, Rao J, Li D, Jing Q, Lu Y, Ji Y. TRIM69 inhibits cataractogenesis by negatively regulating p53. Redox Biol. 2019;22:101157.\u003c/li\u003e\n\u003cli\u003eKnutson KL, Spiegel K, Penev P, Van Cauter E. The metabolic consequences of sleep deprivation. Sleep Med Rev. 2007;11(3):163-78.\u003c/li\u003e\n\u003cli\u003eSayin N, Kara N, Pekel G. Ocular complications of diabetes mellitus. World J Diabetes. 2015;6(1):92-108.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Sleep duration, Cataract, Elderly, Hypertension, China","lastPublishedDoi":"10.21203/rs.3.rs-4954564/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4954564/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn an aging society, cataracts continue to significantly impact the quality of life for an increasing number of elderly individuals. As a risk factor for cataract, hypertension is becoming increasingly prevalent among the elderly year by year. The association between sleep duration and cataract in elderly hypertensive demographic remains unclear and warrants further exploration to aid in strategizing early intervention programs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eBased on China\u0026rsquo;s National Basic Public Health Service Project (NBPHSP), a cross-sectional study was conducted in Jia County, Henan Province, China. A total of 17473 cases aged 65 years and over with hypertension were included in this study. Sleep duration was obtained through questionnaires and information on cataracts was derived from NBPHSP. Three logistic regression models were used to assess the association between sleep duration and cataract. Subgroup analysis and interaction tests were performed to address heterogeneity.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe average self-reported sleep duration was (6.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.80) hours, and the prevalence of cataracts was 11.9%. In the adjusted logistic regression model, elderly hypertensive patients with sleep duration\u0026thinsp;\u0026lt;\u0026thinsp;6 hours had a higher risk of cataract compared to those with sleep duration between 7\u0026ndash;8 hours (OR: 1.39, 95%CI: 1.21\u0026ndash;1.59). However, non-significant association was found between long sleep duration and cataract. The findings from subgroup analysis indicated no significant interaction effect.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eIn rural China, elderly hypertensive patients with a sleep duration of less than 6 hours are at a significantly higher risk of developing cataracts. This finding underscores the importance of monitoring sleep patterns in this population. Promoting adequate sleep duration may be a key strategy in reducing cataract prevalence and improving the overall quality of life for elderly patients with hypertension.\u003c/p\u003e","manuscriptTitle":"Short sleep duration as a risk factor for cataract in the elderly hypertensive patients in rural China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 11:03:00","doi":"10.21203/rs.3.rs-4954564/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36cfa3f9-1bda-4d0f-8999-a23e1c9b829d","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-25T00:38:23+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 11:03:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4954564","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4954564","identity":"rs-4954564","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}