Sleep medication and risk of cognitive decline in community-dwelling older adults: The YAHABA study

Sleep medication and risk of cognitive decline in community-dwelling older adults: The YAHABA study | 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 Article Sleep medication and risk of cognitive decline in community-dwelling older adults: The YAHABA study Yuriko Sato, Hiroshi Akasaka, Kazuki Hosokawa, Takashi Yamaguchi, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5283552/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 The prevalence of dementia has increased in recent years, and sleep disorders are common among older adults. The purpose of this study was to clarify the association between sleep medication and cognitive function in older adults. Community-dwelling older adults were evaluated face-to-face for cognitive function and classified into normal, mild cognitive impairment, and dementia groups. Their history of sleep medication, including benzodiazepines (BZDs), Z-drugs (ZDs), and other medications, was also collected through personal interviews. Statistical analyses using trend analysis and binomial logistic regression analysis with two covariate models were performed to investigate the association between sleep medication and cognitive decline. A total of 869 participants were enrolled, and 12.5% of them were taking sleep medication. Trend analysis showed a significant association between BZD and/or ZD use and cognitive impairment (p = 0.003). Binary logistic regression analysis with multivariate adjustment showed that BZD and/or ZD users had 1.66 times higher odds ratio of cognitive decline compared with non-users (95% confidence interval: 1.07–2.56, p = 0.023). This study demonstrated that sleep medication is associated with a higher risk of cognitive decline in community-dwelling older adults. The findings are important to advance cognitive healthcare management for older adults. Biological sciences/Neuroscience Health sciences/Neurology benzodiazepine Z-drug mild cognitive impairment dementia sleep disturbance older adults Figures Figure 1 Introduction In recent years, there has been a concerning increase in the global prevalence of dementia, with studies indicating that the number of cases reached 57 million by 2019 and is projected to increase to a staggering 150 million by 2050 [ 1 , 2 ]. However, some developed countries have witnessed a promising decline in dementia prevalence; Japan has shown a statistically decreasing trend in prevalence, as referred to in a government press release [ 1 ]. However, despite these positive developments in certain regions, the overall global burden of dementia remains substantial. This upward trend is accompanied by a corresponding increase in the total global cost, estimated at USD 1 trillion in 2018 and expected to escalate to USD 2 trillion by 2030 [ 2 ]. Despite efforts to address this issue, fundamental treatment for dementia remains elusive. Therefore, identifying both risk and protective factors and intervening accordingly is of paramount importance to mitigate the risk of developing dementia. Sleep disturbance is a common medical problem in older adults compared with younger adults under 65 years of age [ 3 , 4 ]. A multi-center study focusing on sleep disturbance in participants aged 65 years or older in the United States showed that the majority of participants experienced various sleep complaints. The study emphasized that sleep disturbance among older adults was closely associated with physical health and mood, as secondary factors [ 5 ]. Sleep quality is important for mental health and cognitive function [ 6 , 7 , 8 ]. A retrospective descriptive analysis of benzodiazepine (BZD) use based on a prescription database showed that approximately 9% of older adults in the community reported taking sleeping pills [ 9 ]. In another study, 16% of adults aged 60 years and older reported being prescribed sleeping pills, and 85% of these reported receiving a prescription from their healthcare provider [ 10 ]. Evidence from the Medical Expenditure Panel Survey, a nationally representative survey of the population of the United States, shows that between 1996 and 2013 the number of adults who reported using prescription sleeping pills increased by 67%, from 8.1 million to 13.5 million [ 11 ]. Among resident adults diagnosed with insomnia, 19% reported using prescription sleep medications, and 27% reported using over-the-counter medications [ 12 ]. The impact of BZDs and related drugs on cognitive function has been highlighted in numerous studies. In recent years, the association has been further strengthened by the publication of multiple systematic reviews. A comprehensive systematic review published in 2021 consolidated these findings, summarizing five systematic reviews with meta-analyses, two additional systematic reviews, and eight non-systematic reviews, providing robust evidence of the association between BZD use and subsequent cognitive decline, including dementia [ 13 ]. On the other hand, a systematic review published in 2020 found no association between Z-drugs (ZDs) and dementia [ 14 ]. Recently, a community-based cohort study of older adults examined the association between the use of sleep medications in the past 2 weeks (including barbiturates, BZDs, antidepressants, other hypnotics, and non-BZD sedatives) and incident dementia, confirming that sleep medication users showed a 1.48 times greater risk of dementia compared with non-users [ 15 ]. Community-based studies are promising to elucidate the relationship between sleep medication use and cognitive decline, but previously conducted studies have been limited and insufficient [ 16 , 17 , 18 ]. Therefore, we aimed to cross-sectionally investigate the association between sleep medication use and cognitive decline among the community-dwelling older adults participating in our community-based cohort study. Results Demographic and medication characteristics We enrolled a total of 869 participants in this study, including 494 females (56.8%). The participants' demographic and health characteristics are shown in Table 1. Their median (interquartile range) age was 72 (68-78) years. The majority of the participants had ≥ 10 years of education and an exercise habit, and there were 158 (18.2%) apolipoprotein E (APOE) ε4 carriers. In the health-records, hypertension was the most common condition, being documented in 654 participants (75.3%), followed by dyslipidemia in 444 participants (51.1%). Sleep disturbance was reported by 159 participants (18.3%), whereas depressive symptoms were less prevalent, being reported by 115 participants (13.2%). Sleep medications were used by 112 participants, including 109 users (12.5%) of BZDs and/or ZDs and 3 users (0.3%) of other agents. There were 67 (7.7%) users of BZDs only, 30 users of ZDs only (3.5%), and 12 users of both BZDs and ZDs (1.4%). Three participants were excluded from the study because they used other agents as sleep medications: one was taking ramelteon, and two were taking suvorexant. Among these three participants, two had also used BZDs (one ramelteon user and one suvorexant user). Differences in characteristics between participants with and without sleep medication The differences in demographic and health characteristics between sleep medication users and non-users are shown in Table 2. Users were significantly more likely to be female (p < 0.001), were older (p < 0.001), had lower educational levels (p < 0.001), and consumed less alcohol (p = 0.003) than non-users. There were no differences in any other demographics, including body mass index and exercise habits, between users and non-users. Depressive symptoms and sleep disturbance were significantly more common in users (p < 0.001 and p < 0.001, respectively). There was no significant difference in the number of APOE ε4 carriers between the user and non-user groups. Associations of demographic and health characteristics with cognitive function The results of trend analyses of the associations of demographic and health characteristics, including sleep medication, with cognitive function are shown in Table 3. The proportions of participants with normal cognitive function, mild cognitive impairment (MCI), and dementia were 64.9% (n = 564), 30.4% (n = 264), and 4.7% (n = 41), respectively. The trend analysis revealed a significant association between the frequency of BZD and/or ZD use and the level of cognitive function (p = 0.003). Significant associations were also found for age (p < 0.001), education (p < 0.001), alcohol consumption (p = 0.003), hypertension (p = 0.034), and depressive symptoms (p = 0.002), whereas no significant association was found for female sex (p = 0.935) or the presence of sleep disorders (p = 0.187). Odds ratios of cognitive decline with BZD and/or ZD use In the binomial logistic regression analyses of the effect of BZD and/or ZD use on cognitive decline, the sex-adjusted model yielded an Odds ratio (OR) (95% CI) of 2.01 (1.34-3.04) (p = 0.001), whereas in the multivariate-adjusted model the OR (95% CI) was 1.66 (1.07-2.56) (p = 0.023) compared with non-users (models 1 and 2 in Table 4, respectively). In a stratified analysis of the effect of BZD and/or ZD use on cognitive decline in model 1, the OR (95% CI) with BZD use was not statistically significant, at 1.52 (0.91-2.53) (p = 0.111), whereas the OR (95% CI) with ZD use was statistically significant, at 2.47 (1.18-5.17) (p = 0.016). In model 2, the OR (95% CI) with BZD use was 1.13 (0.65-1.94) (p = 0.670), which was not statistically significant, whereas the OR (95% CI) with ZD use was statistically significant, at 2.63 (1.21-5.68) (p = 0.014). In the analysis of the effects on cognitive decline of the use of both BZD and ZD, the ORs were significant, at 6.55 (1.75-24.51) (p = 0.005) and 4.66 (1.20-18.06) (p = 0.026) in models 1 and 2, respectively. Discussion We revealed a significant relationship between the use of BZDs and/or ZDs and the severity of cognitive decline using trend analysis. In the trend analyses stratified for the use of BZDs and ZDs, a similar significant statistical relationship was observed. Furthermore, we identified the use of BZDs and/or ZDs as an independent risk factor for cognitive decline through two covariate binominal logistic regression models. In the stratified multivariate-adjusted analysis, ZD use was identified as an independent risk factor, whereas BZD use was not. The results obtained in this study were in line with previous reports on the association between sleep medication and cognitive impairment. In the most recent systematic review, all the reviewed studies showed a significant association between the use of BZDs and dementia, reporting ORs (95% CI) ranging between 1.38 (1.07–1.77) and 1.78 (1.33–2.38) for BZD users [ 13 ]. However, an older systematic review on the association between the use of ZDs and dementia found no statistically significant relationship [ 10 ]. In our study, although the trend analysis supported previous findings, in the multivariate-adjusted model incorporating additional covariates beyond sex, statistical significance disappeared for BZD use, although ZD use remained significantly associated with cognitive decline. Previous reports provide important evidence that should be considered and not entirely dismissed. We could not clarify the reason of the difference, but our community-based study used a different approach, employing direct examinations and verifying medication intakes in personal medication booklets. These methodological differences between studies may account for the differences in results. Changes in the frequency of sleep medication use over time, along with evolving guideline recommendations [ 19 ], may also have potentially influenced the results. Most of the previous studies on the association between the use of BZDs and/or ZDs and cognitive decline used prescription data, such as receipts [ 16 ]. These registry-based database studies have the advantage of larger sample sizes, but they lack detailed, real-world clinical information, including on sleep disturbances, cognitive decline, and medication compliance. Additionally, they typically lack information on MCI, i.e., the preclinical stage of dementia, which is crucial when conducting clinical studies on cognitive function. In this study, the cognitive function of all participants was assessed through face-to-face evaluations conducted by neuropsychologists, and cognitive decline was diagnosed by neurologists. Medication adherence data were also collected from the participants’ personal medication booklets, which were securely maintained. The most significant strength of this study is its community-based cohort design, which facilitates real-world study of the actual situation, thereby reducing bias in participant selection and ensuring relatively uniform participant characteristics. Thus, our real-world findings suggest a potential association between sleep medication and cognitive decline, further indicating that not only classical sleep medication like BZDs but also ZDs may be associated with cognitive decline. A prospective research approach is warranted to confirm these findings. In our trend analyses, age, education level, alcohol consumption, hypertension, and depressive symptoms were found to be significantly associated with the severity of cognitive decline, consistent with previous reports. However, female sex and the presence of sleep disturbance, as evaluated by the Pittsburgh Sleep Quality Index (PSQI), did not show significant associations. Insights into sleep medication use among community-dwelling older adults An age-stratified analysis revealed prevalence rates of sleep disturbances exceeding 20% in Japanese older adults aged 60 years or older. The prevalence rates of sleep medication use in males and females are as follows: 7.5% and 6.5% for those aged 60–69 years, 8.7% and 11.7% for those aged 70–79 years, and 10.2% and 21.8% for those aged 80 years or older, respectively [ 19 ]. The proportion of BZD and/or ZD users in this study was 12.4%, with rates of BZD and ZD use of 9.6% and 4.0%, respectively. Sleep disturbance was observed in 18.4% of all participants, and was significantly more prevalent in BZD and/or ZD users (45.5%) compared with non-users (14.6%). The older adults with insomnia under BZD and/or ZD therapy were distinguished by their advanced age and the presence of depressive mood. These results support many previous studies showing an association between aging and depressive mood [ 20 , 21 ]. Moreover, our trend analysis revealed no significant association of sleep disturbances with the degree of cognitive decline among the normal, MCI, and dementia groups. However, we observed a significant association of the use of sleep medication with the degree of cognitive decline. These findings imply that the use of sleep medication could be a stronger risk factor for cognitive decline than the presence of sleep disturbance alone. Limitations This study differed from previous research in terms of its methodology, which involved direct examination of participants, and it yielded information on real-world outcomes in a community-based setting. However, the relatively small sample size compared with other reports must be acknowledged as a limitation. In addition, we were not able to address the possibility that people with dementia pathology may have sleep disturbances preceding cognitive decline and subsequently resort to sleeping pill usage. Conducting a prospective cohort study in the future will be crucial to verify the causality of the relationship. Conclusion This study involved direct interviews with community-dwelling older adults, allowing for a more real-world investigation. Given the significantly higher risk of cognitive decline among users of sleep medication compared with non-users, this study could prove valuable for geriatric healthcare focused on preventing dementia onset, which is crucial for advancing the management of older adults’ health. Although this cross-sectional study could not establish causation in the relationship between the use of sleep medication and the onset of cognitive decline, it underscores the importance of future prospective studies to elucidate this relationship, given its paramount significance. Methods Study Design We enrolled older adults aged 65 years or more registered in the Yahaba Active Aging and Healthy Brain (YAHABA) study, established in 2016 in Yahaba, a town in a rural area of Iwate Prefecture, Japan. The YAHABA study, a community-based prospective cohort study aimed at clarifying the risk factors and etiology of dementia, cerebrovascular disease, and movement disorders, is being conducted in collaboration with the Japan Prospective Studies Collaboration for Aging and Dementia [ 22 ]. Details are described elsewhere [ 23 ]. A total of 962 participants were enrolled at baseline (registration from 2016 to 2018) and subsequently followed up. After excluding participants with incomplete surveys (12 without a final diagnosis and 25 without medication records) and those with missing data (n = 40), the sample size was reduced to 885. After further excluding individuals aged 90 years or more (n = 13) because of their limited ability to participate effectively, as well as those taking sleeping pills other than BZDs or ZDs (n = 3), the final study sample consisted of 869 participants (Fig. 1 ). This study was approved by the Ethics Committee of Iwate Medical University (approval numbers: HGH28-12, HG2020-017, and MH2022-165) and conformed to the provisions of the Declaration of Helsinki. Written informed consent was obtained from all participants. Demographic measures and definitions We collected social and health-related information from the baseline comprehensive survey of the YAHABA study, including sex, body mass index, education levels, exercise habits, alcohol consumption, and records of medical conditions such as hypertension, diabetes mellitus, dyslipidemia, heart disease, depressive symptoms, and sleep disturbance. The APOE genotype was also evaluated. Education levels were categorized by years of education as ≤ 9 years (junior high school or lower) or ≥ 10 years (high school graduate or higher). Exercise habits were classified as light exercise, such as gardening and stretching, or vigorous exercise such as running and swimming at least once a week (at least 10 minutes per session). Alcohol consumption was defined as self-reported current or former regular drinking of alcohol at least once a month. Hypertension was defined as having a systolic blood pressure ≥ 140 mmHg, a diastolic blood pressure ≥ 90 mmHg, or taking medication for hypertension [ 24 ]. On the basis of the American Diabetes Association 2010 criteria, diabetes mellitus was defined as a fasting blood glucose level ≥ 126 mg/dL, a postprandial blood glucose level ≥ 200 mg/dL, a hemoglobin A1c level ≥ 6.5%, or taking medication for diabetes mellitus [ 25 ]. Dyslipidemia was defined as a low-density lipoprotein cholesterol level ≥ 140 mg/dL, high-density lipoprotein cholesterol level < 40 mg/dL, or triglyceride level ≥ 150 mg/dL [ 26 ]. Heart disease was defined on the basis of a self-reported current or past medical history. Depressive symptoms were evaluated using the Geriatric Depression Scale-short form [ 27 , 28 ], and depression was operationally defined as a Geriatric Depression Scale score ≥ 6 or the current use of antidepressant medication. The participants with depressive symptoms underwent a second screening survey, described elsewhere [ 22 ]. Sleep disturbance was evaluated using the PSQI [ 29 , 30 ] and operationally defined as a PSQI score ≥ 6 or the current use of sleeping medication. The APOE genotype was defined as positive for participants carrying the ε4 allele, encompassing genotypes ε4/ε4, ε4/ε3, and ε4/ε2. Sleep medication survey We collected accurate information on oral medication from the personal medication records of all participants at registration. Personal health record booklets are commonly used in Japan to allow individuals to keep track of their prescriptions and medication compliance. This tool helps ensure the accuracy of medication management and facilitates communication between patients and healthcare providers. In this study, we extracted information on sleep medication. The medications targeted in this study included BZDs, ZDs, and medications of other classes with similar pharmacological effects, such as orexin receptor antagonists, melatonin receptor agonists, and certain antiepileptic drugs used as sleep aids in Japan. Details of the prescriptions, such as regular or as-needed medication and duration of use, were not obtained. Evaluation of cognitive function Cognitive function was evaluated according to the Japan Prospective Studies Collaboration for Aging and Dementia criteria [ 22 ] and classified into three groups: normal, MCI, and dementia. MCI and dementia were diagnosed according to Petersen’s criteria [ 31 ] and the Diagnostic and Statistical Manual of Mental Disorders, Third Edition-Revised [ 32 ], respectively. The Mini Mental Examination was administered to all participants as the first screening. If the total score was ≤ 26, the number of words recalled in the delayed recall test was ≤ 4, the double pentagon and cube were copied incorrectly, or other behaviors or words that were suspicious of cognitive decline were observed, a detailed cognitive function test was conducted as the second screening. Cognitive tests including the Wechsler Memory Scale-Revised Logical Memory I and II, verbal fluency, general knowledge, and pareidolia tests were administered. Activities of daily living and instrumental activities of daily living were assessed on the basis of information obtained from the participants and their families. Morphological evaluation was performed using brain magnetic resonance imaging. Trained neurologists examined the participants and made a comprehensive diagnosis of MCI or dementia on the basis of all the results. Statistical Analysis For basic statistical analyses, the Mann-Whitney U test (for continuous variables) and the chi-square test (for categorical variables) were performed. The Jonckheere-Terpstra trend test (for continuous variables) and linear regression analysis (for categorical variables) were used to compare trends in demographic and medical characteristics among the three cognitive groups (normal, MCI, and dementia). Binomial logistic regression analysis of the three groups was also performed, with participant characteristics as covariates. The presence of BZD and/or ZD was considered the explanatory variable, whereas cognitive decline (diagnosis of MCI or dementia) was the objective variable. We constructed two covariate models: a sex-adjusted model and a multivariable-adjusted model with sex, years of education, alcohol consumption, hypertension, and depressive symptoms as covariates. ORs and 95% confidence intervals (CIs) for participants with MCI and dementia were obtained using normal participants as the reference group. Three participants using other classes of sleep medications were categorized separately from those using BZD and/or ZD. Because of the number of such participants, they were excluded from the binomial logistic regression analysis as they could not be adequately analyzed. All statistical analyses were performed using IBM SPSS software (version 27.0.1; IBM Japan, Tokyo, Japan). For all analyses, a two-sided p-value < 0.05 was considered statistically significant. Declarations Competing interests The authors declare no competing interests. Funding This study was supported by Grants-in-Aid from the Japan Agency for Medical Research and Development (JP24dk0207053). The funder had no role in the design of the study, the collection, analysis, and interpretation of the data, or the writing of the manuscript. Funding acquisition: T.M. Author Contribution Conceptualization: Y.S., H.A., N.I., and T.M.Data curation: Y.S., H.A., K.H., T.Y., R.N., T.T., E.H., M.S., N.I., and T.M.Formal analysis: Y.S., H.A., and T.M.Writing - original draft: Y.S. and T.M.Funding acquisition: T.M.Investigation: Y.S., H.A., K.H., T.Y., R.N., T.T., E.H., M.S., N.I., and T.M.Methodology: Y.S., H.A., N.I., and T.M.Project administration: Y.S., H.A., N.I., and T.M.Resources: NoneSoftware: NoneSupervision: T.M.Validation: T.M.Writing - review & editing: All authors reviewed and approved the final draft of the manuscript. Acknowledgement We thank Akie Sakamoto and Kuniko Watanabe (Division of Neurology and Gerontology, Department of Internal Medicine, School of Medicine, and Iwate Medical University) and Noriko Imakawa, Sayuri Tanii, and Sayaka Sasaki (Sawayaka House Office) for general support with the study. We also thank Michael Irvine, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript. Data Availability The data used in this study are not publicly available due to privacy and ethical restrictions. Any request to access the data must be directed to the corresponding author and will require appropriate permissions. References GBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health . 7, e105-e125. doi:10.1016/S2468-2667(21)00249-8 (2022). Prince, M., et al. World Alzheimer Report 2015. The Global Impact of Dementia: an analysis of prevalence, incidence, cost and trends. Alzheimer’s Disease International https://www.alzint.org/u/WorldAlzheimerReport2015.pdf (2015). Miner, B. & Kryger, M. H. Sleep in the aging population. Sleep Med Clin . 12, 31-38. https://doi.org/10.1016/j.jsmc.2016.10.008 (2017). Morin, C. M., LeBlanc, M., Daley, M., Gregoire, J. P. & Mérette, C. 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Psychiatry Res . 28, 193-213. https://doi.org/10.1016/0165-1781(89)90047-4 (1989). Doi, Y., et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh sleep quality index (PSQI-J) in psychiatric disordered and control subjects. Psychiatry Res . 97, 165-172. https://doi.org/10.1016/s0165-1781(00)00232-8 (2000). Petersen, R. C., et al. Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review). Report of the quality standards subcommittee of the American Academy of Neurology. Neurology . 56, 1133-1142. https://doi.org/10.1212/wnl.56.9.1133 (2001). American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders 3rd edn . (American Psychiatric Association, Washington, DC, 1987). Tables Table 1. Demographics and health profiles Characteristics Total (n = 869) Demographic Female sex, n (%) 494 (56.8) Age, years, median (IQR) 72 (68 - 78) BMI, m 2 /kg, median (IQR) 24.0 (21.9 - 26.3) Education, years ≤ 9, n (%) 324 (37.3) ≥10, n (%) 545 (62.7) Exercise habits, n (%) yes 575 (66.2) no 297 (33.8) Alcohol consumption, n (%) yes 377 (43.4) no 492 (56.6) APOE ε4 carrier, n (%) 158 (18.2) Health profile Hypertension, n (%) 654 (75.3) Diabetes mellitus, n (%) 141 (16.2) Dyslipidemia, n (%) 444 (51.1) Heart disease, n (%) 85 (9.8) Depression, n (%) 115 (13.2) Sleep disturbance, n (%) 159 (18.3) Sleep medication, n (%) 112 (12.9) BZD and/or ZD, n (%) 109 (12.5) BZD, n (%) 67 (7.7) ZD, n (%) 30 (3.5) BZD and ZD, (%) 12 (1.4) Other agents, n (%) 3 (0.3) Table 2. Difference in demographics and health profiles between sleep medication users and non-users Characteristics Sleep medication p value Users (n = 109) Non-users (n = 760) Demographic Female sex, n (%) 82 (75.2) 412 (54.2) <0.001 Age, years, mean (IQR) 76 (70 - 80) 71 (68 - 77) <0.001 BMI, m 2 /kg, mean (IQR) 24.1 (21.9 - 26.8) 24.0 (21.9 - 26.3) 0.527 Education, years <0.001 ≤ 9, n (%) 59 (54.1) 265 (34.9) ≥10, n (%) 50 (45.9) 495 (65.1) Exercise habits, n (%) 75 (68.8) 500 (65.8) 0.533 Alcohol consumption, n (%) 33 (30.3) 344 (45.3) 0.003 APOE ε4 carrier, n (%) 16 (14.7) 142 (18.7) 0.311 Health profile Hypertension, n (%) 85 (78.0) 569 (74.9) 0.481 Diabetes mellitus, n (%) 14 (12.8) 127 (16.7) 0.306 Dyslipidemia, n (%) 56 (51.4) 388 (51.1) 0.950 Heart disease, n (%) 10 (9.2) 75 (9.9) 0.820 Depressive symptoms, n (%) 29 (26.6) 86 (11.3) <0.001 Sleep disturbance, n (%) 48 (44.0) 111 (14.6) <0.001 Table 3. Association between demographics or health profiles and cognitive function Characteristics Normal (n = 564) MCI (n = 264) Dementia (n = 41) p value for trend Demographic Female sex, n (%) 329 (58.3) 134 (50.8) 31 (75.6) 0.935 Age, years, mean (IQR) 70 (67 - 75) 76 (69 - 81) 81 (76 - 84) <0.001 BMI, m 2 /kg, mean (IQR) 24.0 (21.8 - 26.2) 24.3 (22.0 - 26.6) 23.9 (22.1 - 25.7) 0.483 Education, years <0.001 ≤ 9, n (%) 163 (28.9) 129 (48.9) 32 (78.0) ≥10, n (%) 401 (71.1) 135 (51.1) 9 (22.0) Exercise habits, n (%) 371 (65.8) 173 (65.5) 31 (75.6) 0.453 Alcohol consumption, n (%) 261 (46.3) 107 (40.5) 9 (22.0) 0.003 APOE ε4 carrier, n (%) 97 (17.2) 51 (19.3) 10 (24.4) 0.219 Health Profile Hypertension, n (%) 410 (72.7) 212 (80.3) 32 (78.0) 0.034 Diabetes mellitus, n (%) 92 (16.3) 38 (14.4) 11 (26.8) 0.539 Dyslipidemia, n (%) 288 (51.1) 143 (54.2) 13 (31.7) 0.361 Heart disease, n (%) 44 (7.8) 39 (14.8) 2 (4.9) 0.071 Depressive symptoms, n (%) 61 (10.8) 44 (16.7) 10 (24.4) 0.002 Sleep disturbance, n (%) 100 (17.7) 46 (17.4) 13 (31.7) 0.187 Sleep medication BZD and/or ZD, n (%) 56 (9.9) 46 (17.4) 7 (17.1) 0.003 BZD, n (%) 39 (6.9) 25 (9.5) 3 (7.3) 0.267 ZD, n (%) 14 (2.5) 16 (6.1) 0 (0) 0.156 BZD and ZD, n (%) 3 (0.5) 5 (1.9) 4 (9.8) <0.001 Table 4. Estimated odds ratios of sleep medication for cognitive decline Model 1 Model 2 OR, 95% CI p value OR, 95% CI p value No sleep medication reference reference BZD and/or ZD 2.01, 1.34 - 3.04 0.001 1.66, 1.07 - 2.56 0.023 No sleep medication reference reference BZD 1.52, 0.91 - 2.53 0.111 1.13, 0.65 - 1.94 0.670 ZD 2.47, 1.18 - 5.17 0.016 2.63, 1.21 - 5.68 0.014 BZD and ZD 6.55, 1.75 - 24.51 0.005 4.66, 1.20 - 18.06 0.026 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5283552","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":373710161,"identity":"5f4c8e35-7bee-4d77-a79f-3a093862984d","order_by":0,"name":"Yuriko Sato","email":"","orcid":"","institution":"Iwate Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuriko","middleName":"","lastName":"Sato","suffix":""},{"id":373710162,"identity":"df67bf56-d17f-4aeb-bc00-5a10e352ac0d","order_by":1,"name":"Hiroshi Akasaka","email":"","orcid":"","institution":"Iwate Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Akasaka","suffix":""},{"id":373710163,"identity":"32d6d406-530a-4dd7-b124-b607fd96fa95","order_by":2,"name":"Kazuki Hosokawa","email":"","orcid":"","institution":"Iwate Medical University","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Hosokawa","suffix":""},{"id":373710164,"identity":"36393872-0a85-449e-915c-5105d8136539","order_by":3,"name":"Takashi Yamaguchi","email":"","orcid":"","institution":"Iwate Medical University","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Yamaguchi","suffix":""},{"id":373710165,"identity":"1c7cfca1-3a95-4bfa-84b0-a448624b58f0","order_by":4,"name":"Ryota Nozaki","email":"","orcid":"","institution":"Iwate Medical 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14:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5283552/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5283552/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68694190,"identity":"0388536f-dcde-41c5-9acf-0c1ef882b04f","added_by":"auto","created_at":"2024-11-11 06:34:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32394,"visible":true,"origin":"","legend":"\u003cp\u003eSelection process of the eligible participants for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEligible participants were defined along this flow. n, number.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5283552/v1/09c33793de67caededbd4588.png"},{"id":76476695,"identity":"466c4506-71f5-4774-ab6e-8badf44d39a6","added_by":"auto","created_at":"2025-02-17 13:54:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":856934,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5283552/v1/36b006f4-a2db-495a-9e42-a54f608131bb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sleep medication and risk of cognitive decline in community-dwelling older adults: The YAHABA study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn recent years, there has been a concerning increase in the global prevalence of dementia, with studies indicating that the number of cases reached 57\u0026nbsp;million by 2019 and is projected to increase to a staggering 150\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, some developed countries have witnessed a promising decline in dementia prevalence; Japan has shown a statistically decreasing trend in prevalence, as referred to in a government press release [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, despite these positive developments in certain regions, the overall global burden of dementia remains substantial. This upward trend is accompanied by a corresponding increase in the total global cost, estimated at USD 1 trillion in 2018 and expected to escalate to USD 2 trillion by 2030 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite efforts to address this issue, fundamental treatment for dementia remains elusive. Therefore, identifying both risk and protective factors and intervening accordingly is of paramount importance to mitigate the risk of developing dementia.\u003c/p\u003e \u003cp\u003eSleep disturbance is a common medical problem in older adults compared with younger adults under 65 years of age [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. A multi-center study focusing on sleep disturbance in participants aged 65 years or older in the United States showed that the majority of participants experienced various sleep complaints. The study emphasized that sleep disturbance among older adults was closely associated with physical health and mood, as secondary factors [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Sleep quality is important for mental health and cognitive function [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A retrospective descriptive analysis of benzodiazepine (BZD) use based on a prescription database showed that approximately 9% of older adults in the community reported taking sleeping pills [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In another study, 16% of adults aged 60 years and older reported being prescribed sleeping pills, and 85% of these reported receiving a prescription from their healthcare provider [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Evidence from the Medical Expenditure Panel Survey, a nationally representative survey of the population of the United States, shows that between 1996 and 2013 the number of adults who reported using prescription sleeping pills increased by 67%, from 8.1\u0026nbsp;million to 13.5\u0026nbsp;million [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among resident adults diagnosed with insomnia, 19% reported using prescription sleep medications, and 27% reported using over-the-counter medications [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe impact of BZDs and related drugs on cognitive function has been highlighted in numerous studies. In recent years, the association has been further strengthened by the publication of multiple systematic reviews. A comprehensive systematic review published in 2021 consolidated these findings, summarizing five systematic reviews with meta-analyses, two additional systematic reviews, and eight non-systematic reviews, providing robust evidence of the association between BZD use and subsequent cognitive decline, including dementia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. On the other hand, a systematic review published in 2020 found no association between Z-drugs (ZDs) and dementia [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Recently, a community-based cohort study of older adults examined the association between the use of sleep medications in the past 2 weeks (including barbiturates, BZDs, antidepressants, other hypnotics, and non-BZD sedatives) and incident dementia, confirming that sleep medication users showed a 1.48 times greater risk of dementia compared with non-users [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Community-based studies are promising to elucidate the relationship between sleep medication use and cognitive decline, but previously conducted studies have been limited and insufficient [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Therefore, we aimed to cross-sectionally investigate the association between sleep medication use and cognitive decline among the community-dwelling older adults participating in our community-based cohort study.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDemographic and medication characteristics\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe enrolled a total of 869 participants in this study, including 494 females (56.8%). The participants\u0026apos; demographic and health characteristics are shown in Table 1. Their median (interquartile range) age was 72 (68-78) years. The majority of the participants had \u0026ge; 10 years of education and an exercise habit, and there were 158 (18.2%) apolipoprotein E (APOE) \u0026epsilon;4 carriers. In the health-records, hypertension was the most common condition, being documented in 654 participants (75.3%), followed by dyslipidemia in 444 participants (51.1%). Sleep disturbance was reported by 159 participants (18.3%), whereas depressive symptoms were less prevalent, being reported by 115 participants (13.2%). Sleep medications were used by 112 participants, including 109 users (12.5%) of BZDs and/or ZDs and 3 users (0.3%) of other agents. There were 67 (7.7%) users of BZDs only, 30 users of ZDs only (3.5%), and 12 users of both BZDs and ZDs (1.4%). Three participants were excluded from the study because they used other agents as sleep medications: one was taking ramelteon, and two were taking suvorexant. Among these three participants, two had also used BZDs (one ramelteon user and one suvorexant user).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eDifferences in characteristics between participants with and without sleep medication\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe differences in demographic and health characteristics between sleep medication users and non-users are shown in Table 2. Users were significantly more likely to be female (p \u0026lt; 0.001), were older (p \u0026lt; 0.001), had lower educational levels (p \u0026lt; 0.001), and consumed less alcohol (p = 0.003) than non-users. There were no differences in any other demographics, including body mass index and exercise habits, between users and non-users. Depressive symptoms and sleep disturbance were significantly more common in users (p \u0026lt; 0.001 and p \u0026lt; 0.001, respectively). There was no significant difference in the number of APOE \u0026epsilon;4 carriers between the user and non-user groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAssociations of demographic and health characteristics with cognitive function\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of trend analyses of the associations of demographic and health characteristics, including sleep medication, with cognitive function are shown in Table 3. The proportions of participants with normal cognitive function, mild cognitive impairment (MCI), and dementia were 64.9% (n = 564), 30.4% (n = 264), and 4.7% (n = 41), respectively. The trend analysis revealed a significant association between the frequency of BZD and/or ZD use and the level of cognitive function (p = 0.003). Significant associations were also found for age (p \u0026lt; 0.001), education (p \u0026lt; 0.001), alcohol consumption (p = 0.003), hypertension (p = 0.034), and depressive symptoms (p = 0.002), whereas no significant association was found for female sex (p = 0.935) or the presence of sleep disorders (p = 0.187).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eOdds ratios of cognitive decline with BZD and/or ZD use\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the binomial logistic regression analyses of the effect of BZD and/or ZD use on cognitive decline, the sex-adjusted model yielded an Odds ratio (OR) (95% CI) of 2.01 (1.34-3.04) (p = 0.001), whereas in the multivariate-adjusted model the OR (95% CI) was 1.66 (1.07-2.56) (p = 0.023) compared with non-users (models 1 and 2 in Table 4, respectively). In a stratified analysis of the effect of BZD and/or ZD use on cognitive decline in model 1, the OR (95% CI) with BZD use was not statistically significant, at 1.52 (0.91-2.53) (p = 0.111), whereas the OR (95% CI) with ZD use was statistically significant, at 2.47 (1.18-5.17) (p = 0.016). In model 2, the OR (95% CI) with BZD use was 1.13 (0.65-1.94) (p = 0.670), which was not statistically significant, whereas the OR (95% CI) with ZD use was statistically significant, at 2.63 (1.21-5.68) (p = 0.014). In the analysis of the effects on cognitive decline of the use of both BZD and ZD, the ORs were significant, at 6.55 (1.75-24.51) (p = 0.005) and 4.66 (1.20-18.06) (p = 0.026) in models 1 and 2, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe revealed a significant relationship between the use of BZDs and/or ZDs and the severity of cognitive decline using trend analysis. In the trend analyses stratified for the use of BZDs and ZDs, a similar significant statistical relationship was observed. Furthermore, we identified the use of BZDs and/or ZDs as an independent risk factor for cognitive decline through two covariate binominal logistic regression models. In the stratified multivariate-adjusted analysis, ZD use was identified as an independent risk factor, whereas BZD use was not. The results obtained in this study were in line with previous reports on the association between sleep medication and cognitive impairment. In the most recent systematic review, all the reviewed studies showed a significant association between the use of BZDs and dementia, reporting ORs (95% CI) ranging between 1.38 (1.07\u0026ndash;1.77) and 1.78 (1.33\u0026ndash;2.38) for BZD users [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, an older systematic review on the association between the use of ZDs and dementia found no statistically significant relationship [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In our study, although the trend analysis supported previous findings, in the multivariate-adjusted model incorporating additional covariates beyond sex, statistical significance disappeared for BZD use, although ZD use remained significantly associated with cognitive decline. Previous reports provide important evidence that should be considered and not entirely dismissed. We could not clarify the reason of the difference, but our community-based study used a different approach, employing direct examinations and verifying medication intakes in personal medication booklets. These methodological differences between studies may account for the differences in results. Changes in the frequency of sleep medication use over time, along with evolving guideline recommendations [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], may also have potentially influenced the results. Most of the previous studies on the association between the use of BZDs and/or ZDs and cognitive decline used prescription data, such as receipts [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These registry-based database studies have the advantage of larger sample sizes, but they lack detailed, real-world clinical information, including on sleep disturbances, cognitive decline, and medication compliance. Additionally, they typically lack information on MCI, i.e., the preclinical stage of dementia, which is crucial when conducting clinical studies on cognitive function. In this study, the cognitive function of all participants was assessed through face-to-face evaluations conducted by neuropsychologists, and cognitive decline was diagnosed by neurologists. Medication adherence data were also collected from the participants\u0026rsquo; personal medication booklets, which were securely maintained. The most significant strength of this study is its community-based cohort design, which facilitates real-world study of the actual situation, thereby reducing bias in participant selection and ensuring relatively uniform participant characteristics. Thus, our real-world findings suggest a potential association between sleep medication and cognitive decline, further indicating that not only classical sleep medication like BZDs but also ZDs may be associated with cognitive decline. A prospective research approach is warranted to confirm these findings.\u003c/p\u003e \u003cp\u003eIn our trend analyses, age, education level, alcohol consumption, hypertension, and depressive symptoms were found to be significantly associated with the severity of cognitive decline, consistent with previous reports. However, female sex and the presence of sleep disturbance, as evaluated by the Pittsburgh Sleep Quality Index (PSQI), did not show significant associations.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInsights into sleep medication use among community-dwelling older adults\u003c/h2\u003e \u003cp\u003eAn age-stratified analysis revealed prevalence rates of sleep disturbances exceeding 20% in Japanese older adults aged 60 years or older. The prevalence rates of sleep medication use in males and females are as follows: 7.5% and 6.5% for those aged 60\u0026ndash;69 years, 8.7% and 11.7% for those aged 70\u0026ndash;79 years, and 10.2% and 21.8% for those aged 80 years or older, respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The proportion of BZD and/or ZD users in this study was 12.4%, with rates of BZD and ZD use of 9.6% and 4.0%, respectively. Sleep disturbance was observed in 18.4% of all participants, and was significantly more prevalent in BZD and/or ZD users (45.5%) compared with non-users (14.6%). The older adults with insomnia under BZD and/or ZD therapy were distinguished by their advanced age and the presence of depressive mood. These results support many previous studies showing an association between aging and depressive mood [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, our trend analysis revealed no significant association of sleep disturbances with the degree of cognitive decline among the normal, MCI, and dementia groups. However, we observed a significant association of the use of sleep medication with the degree of cognitive decline. These findings imply that the use of sleep medication could be a stronger risk factor for cognitive decline than the presence of sleep disturbance alone.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eThis study differed from previous research in terms of its methodology, which involved direct examination of participants, and it yielded information on real-world outcomes in a community-based setting. However, the relatively small sample size compared with other reports must be acknowledged as a limitation. In addition, we were not able to address the possibility that people with dementia pathology may have sleep disturbances preceding cognitive decline and subsequently resort to sleeping pill usage. Conducting a prospective cohort study in the future will be crucial to verify the causality of the relationship.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study involved direct interviews with community-dwelling older adults, allowing for a more real-world investigation. Given the significantly higher risk of cognitive decline among users of sleep medication compared with non-users, this study could prove valuable for geriatric healthcare focused on preventing dementia onset, which is crucial for advancing the management of older adults\u0026rsquo; health. Although this cross-sectional study could not establish causation in the relationship between the use of sleep medication and the onset of cognitive decline, it underscores the importance of future prospective studies to elucidate this relationship, given its paramount significance.\u003c/p\u003e "},{"header":"Methods","content":" \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003eWe enrolled older adults aged 65 years or more registered in the Yahaba Active Aging and Healthy Brain (YAHABA) study, established in 2016 in Yahaba, a town in a rural area of Iwate Prefecture, Japan. The YAHABA study, a community-based prospective cohort study aimed at clarifying the risk factors and etiology of dementia, cerebrovascular disease, and movement disorders, is being conducted in collaboration with the Japan Prospective Studies Collaboration for Aging and Dementia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Details are described elsewhere [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A total of 962 participants were enrolled at baseline (registration from 2016 to 2018) and subsequently followed up. After excluding participants with incomplete surveys (12 without a final diagnosis and 25 without medication records) and those with missing data (n\u0026thinsp;=\u0026thinsp;40), the sample size was reduced to 885. After further excluding individuals aged 90 years or more (n\u0026thinsp;=\u0026thinsp;13) because of their limited ability to participate effectively, as well as those taking sleeping pills other than BZDs or ZDs (n\u0026thinsp;=\u0026thinsp;3), the final study sample consisted of 869 participants (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Ethics Committee of Iwate Medical University (approval numbers: HGH28-12, HG2020-017, and MH2022-165) and conformed to the provisions of the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDemographic measures and definitions\u003c/h2\u003e \u003cp\u003eWe collected social and health-related information from the baseline comprehensive survey of the YAHABA study, including sex, body mass index, education levels, exercise habits, alcohol consumption, and records of medical conditions such as hypertension, diabetes mellitus, dyslipidemia, heart disease, depressive symptoms, and sleep disturbance. The APOE genotype was also evaluated.\u003c/p\u003e \u003cp\u003eEducation levels were categorized by years of education as \u0026le;\u0026thinsp;9 years (junior high school or lower) or \u0026ge;\u0026thinsp;10 years (high school graduate or higher). Exercise habits were classified as light exercise, such as gardening and stretching, or vigorous exercise such as running and swimming at least once a week (at least 10 minutes per session). Alcohol consumption was defined as self-reported current or former regular drinking of alcohol at least once a month. Hypertension was defined as having a systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, a diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg, or taking medication for hypertension [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. On the basis of the American Diabetes Association 2010 criteria, diabetes mellitus was defined as a fasting blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, a postprandial blood glucose level\u0026thinsp;\u0026ge;\u0026thinsp;200 mg/dL, a hemoglobin A1c level\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or taking medication for diabetes mellitus [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Dyslipidemia was defined as a low-density lipoprotein cholesterol level\u0026thinsp;\u0026ge;\u0026thinsp;140 mg/dL, high-density lipoprotein cholesterol level\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL, or triglyceride level\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Heart disease was defined on the basis of a self-reported current or past medical history. Depressive symptoms were evaluated using the Geriatric Depression Scale-short form [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and depression was operationally defined as a Geriatric Depression Scale score\u0026thinsp;\u0026ge;\u0026thinsp;6 or the current use of antidepressant medication. The participants with depressive symptoms underwent a second screening survey, described elsewhere [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Sleep disturbance was evaluated using the PSQI [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and operationally defined as a PSQI score\u0026thinsp;\u0026ge;\u0026thinsp;6 or the current use of sleeping medication. The APOE genotype was defined as positive for participants carrying the ε4 allele, encompassing genotypes ε4/ε4, ε4/ε3, and ε4/ε2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSleep medication survey\u003c/h2\u003e \u003cp\u003eWe collected accurate information on oral medication from the personal medication records of all participants at registration. Personal health record booklets are commonly used in Japan to allow individuals to keep track of their prescriptions and medication compliance. This tool helps ensure the accuracy of medication management and facilitates communication between patients and healthcare providers. In this study, we extracted information on sleep medication. The medications targeted in this study included BZDs, ZDs, and medications of other classes with similar pharmacological effects, such as orexin receptor antagonists, melatonin receptor agonists, and certain antiepileptic drugs used as sleep aids in Japan. Details of the prescriptions, such as regular or as-needed medication and duration of use, were not obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of cognitive function\u003c/h2\u003e \u003cp\u003eCognitive function was evaluated according to the Japan Prospective Studies Collaboration for Aging and Dementia criteria [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and classified into three groups: normal, MCI, and dementia. MCI and dementia were diagnosed according to Petersen\u0026rsquo;s criteria [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the Diagnostic and Statistical Manual of Mental Disorders, Third Edition-Revised [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], respectively. The Mini Mental Examination was administered to all participants as the first screening. If the total score was \u0026le;\u0026thinsp;26, the number of words recalled in the delayed recall test was \u0026le;\u0026thinsp;4, the double pentagon and cube were copied incorrectly, or other behaviors or words that were suspicious of cognitive decline were observed, a detailed cognitive function test was conducted as the second screening. Cognitive tests including the Wechsler Memory Scale-Revised Logical Memory I and II, verbal fluency, general knowledge, and pareidolia tests were administered. Activities of daily living and instrumental activities of daily living were assessed on the basis of information obtained from the participants and their families. Morphological evaluation was performed using brain magnetic resonance imaging. Trained neurologists examined the participants and made a comprehensive diagnosis of MCI or dementia on the basis of all the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eFor basic statistical analyses, the Mann-Whitney U test (for continuous variables) and the chi-square test (for categorical variables) were performed. The Jonckheere-Terpstra trend test (for continuous variables) and linear regression analysis (for categorical variables) were used to compare trends in demographic and medical characteristics among the three cognitive groups (normal, MCI, and dementia). Binomial logistic regression analysis of the three groups was also performed, with participant characteristics as covariates. The presence of BZD and/or ZD was considered the explanatory variable, whereas cognitive decline (diagnosis of MCI or dementia) was the objective variable. We constructed two covariate models: a sex-adjusted model and a multivariable-adjusted model with sex, years of education, alcohol consumption, hypertension, and depressive symptoms as covariates. ORs and 95% confidence intervals (CIs) for participants with MCI and dementia were obtained using normal participants as the reference group. Three participants using other classes of sleep medications were categorized separately from those using BZD and/or ZD. Because of the number of such participants, they were excluded from the binomial logistic regression analysis as they could not be adequately analyzed. All statistical analyses were performed using IBM SPSS software (version 27.0.1; IBM Japan, Tokyo, Japan). For all analyses, a two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003ch2\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by Grants-in-Aid from the Japan Agency for Medical Research and Development (JP24dk0207053). The funder had no role in the design of the study, the collection, analysis, and interpretation of the data, or the writing of the manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eacquisition: T.M.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: Y.S., H.A., N.I., and T.M.Data curation: Y.S., H.A., K.H., T.Y., R.N., T.T., E.H., M.S., N.I., and T.M.Formal analysis: Y.S., H.A., and T.M.Writing - original draft: Y.S. and T.M.Funding acquisition: T.M.Investigation: Y.S., H.A., K.H., T.Y., R.N., T.T., E.H., M.S., N.I., and T.M.Methodology: Y.S., H.A., N.I., and T.M.Project administration: Y.S., H.A., N.I., and T.M.Resources: NoneSoftware: NoneSupervision: T.M.Validation: T.M.Writing - review \u0026amp; editing: All authors reviewed and approved the final draft of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe thank Akie Sakamoto and Kuniko Watanabe (Division of Neurology and Gerontology, Department of Internal Medicine, School of Medicine, and Iwate Medical University) and Noriko Imakawa, Sayuri Tanii, and Sayaka Sasaki (Sawayaka House Office) for general support with the study. We also thank Michael Irvine, PhD, from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used in this study are not publicly available due to privacy and ethical restrictions. Any request to access the data must be directed to the corresponding author and will require appropriate permissions.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eGBD 2019 Dementia Forecasting Collaborators. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. \u003cem\u003eLancet Public Health\u003c/em\u003e. \u003cstrong\u003e7,\u003c/strong\u003e e105-e125. doi:10.1016/S2468-2667(21)00249-8 (2022).\u003c/li\u003e\n \u003cli\u003ePrince, M., et al.\u0026nbsp;World Alzheimer Report 2015. 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Reliability and validity of the Japanese version of Geriatric Depression Scale-Short Version (GDS-S-J). \u003cem\u003eJapanese Soc Cogn Neurosci\u003c/em\u003e. \u003cstrong\u003e11,\u003c/strong\u003e 87-90. https://doi.org/10.11253/NINCHISHINKEIKAGAKU.11.87 (2009).\u003c/li\u003e\n \u003cli\u003eBuysse, D. J., Reynolds, C. F. 3rd, Monk, T. H., Berman, S. R. \u0026amp; Kupfer, D. J. The Pittsburgh sleep quality index: a new instrument for psychiatric practice and research. \u003cem\u003ePsychiatry Res\u003c/em\u003e. \u003cstrong\u003e28,\u003c/strong\u003e 193-213. https://doi.org/10.1016/0165-1781(89)90047-4 (1989).\u003c/li\u003e\n \u003cli\u003eDoi, Y., et al. Psychometric assessment of subjective sleep quality using the Japanese version of the Pittsburgh sleep quality index (PSQI-J) in psychiatric disordered and control subjects.\u003cem\u003e\u0026nbsp;Psychiatry Res\u003c/em\u003e. \u003cstrong\u003e97,\u003c/strong\u003e 165-172.\u0026nbsp;https://doi.org/10.1016/s0165-1781(00)00232-8 (2000).\u003c/li\u003e\n \u003cli\u003ePetersen, R. C., et al. Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review). Report of the quality standards subcommittee of the American Academy of Neurology.\u003cem\u003e\u0026nbsp;Neurology\u003c/em\u003e. \u003cstrong\u003e56,\u003c/strong\u003e 1133-1142.\u0026nbsp;https://doi.org/10.1212/wnl.56.9.1133 (2001).\u003c/li\u003e\n \u003cli\u003eAmerican Psychiatric Association. \u003cem\u003eDiagnostic and Statistical Manual of Mental Disorders 3rd edn\u003c/em\u003e. (American Psychiatric Association, Washington, DC, 1987).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1.\u0026nbsp;Demographics and health profiles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"529\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003eTotal (n = 869)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e494 (56.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eAge, years, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e72 (68 - 78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eBMI, m\u003csup\u003e2\u003c/sup\u003e/kg, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e24.0 (21.9 - 26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eEducation,\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003e\u0026le; 9, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e324 (37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003e\u0026ge;10, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e545 (62.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eExercise habits, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e575 (66.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e297 (33.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e377 (43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eno\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e492 (56.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eAPOE \u0026epsilon;4 carrier, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e158 (18.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eHealth profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e654 (75.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e141 (16.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e444 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eHeart disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e85 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eDepression, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e115 (13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eSleep disturbance, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e159 (18.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eSleep medication, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e112 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eBZD and/or ZD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e109 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eBZD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e67 (7.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eZD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e30 (3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eBZD and ZD, (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e12 (1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 48.9603%;\"\u003e\n \u003cp\u003eOther agents, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51.0397%;\"\u003e\n \u003cp\u003e3 (0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2.\u0026nbsp;Difference in demographics and health profiles between sleep medication users and non-users\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"877\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 247px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 514px;\"\u003e\n \u003cp\u003eSleep medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 116px;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eUsers (n = 109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003eNon-users (n = 760)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e82 (75.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e412 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eAge, years, mean (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e76 (70 - 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e71 (68 - 77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eBMI, m\u003csup\u003e2\u003c/sup\u003e/kg, mean (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e24.1 (21.9 - 26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e24.0 (21.9 - 26.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eEducation,\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003e\u0026le; 9, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e59 (54.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e265 (34.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003e\u0026ge;10, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e50 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e495 (65.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eExercise habits, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e75 (68.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e500 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e33 (30.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e344 (45.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eAPOE \u0026epsilon;4 carrier, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e16 (14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e142 (18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eHealth profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e85 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e569 (74.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e14 (12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e127 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.306\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e56 (51.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e388 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eHeart disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e10 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e75 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.820\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eDepressive symptoms, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e29 (26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e86 (11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 247px;\"\u003e\n \u003cp\u003eSleep disturbance, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e48 (44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 257px;\"\u003e\n \u003cp\u003e111 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Association between demographics or health profiles and cognitive function\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"877\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003eNormal (n = 564)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003eMCI (n = 264)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003eDementia (n = 41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003ep value for trend\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eDemographic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eFemale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e329 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e134 (50.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e31 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.935\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eAge, years, mean (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e70 (67 - 75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e76 (69 - 81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e81 (76 - 84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eBMI, m\u003csup\u003e2\u003c/sup\u003e/kg, mean (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e24.0 (21.8 - 26.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e24.3 (22.0 - 26.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e23.9 (22.1 - 25.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eEducation,\u0026nbsp;years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003e\u0026le; 9, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e163 (28.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e129 (48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e32 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003e\u0026ge;10, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e401 (71.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e135 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e9 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eExercise habits, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e371 (65.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e173 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e31 (75.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.453\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eAlcohol consumption, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e261 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e107 (40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e9 (22.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eAPOE \u0026epsilon;4 carrier, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e97 (17.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e51 (19.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eHealth Profile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e410 (72.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e212 (80.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e32 (78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eDiabetes mellitus, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e92 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e38 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eDyslipidemia, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e288 (51.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e143 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eHeart disease, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e44 (7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e39 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e2 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eDepressive symptoms, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e61 (10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e44 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e10 (24.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eSleep disturbance, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e100 (17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e46 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e13 (31.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eSleep medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eBZD and/or ZD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e56 (9.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e46 (17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e7 (17.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eBZD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e39 (6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e25 (9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e3 (7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eZD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e14 (2.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e16 (6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 27.5941%;\"\u003e\n \u003cp\u003eBZD and ZD, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e3 (0.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e5 (1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.7263%;\"\u003e\n \u003cp\u003e4 (9.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.2269%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Estimated odds ratios of sleep medication for cognitive decline\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"680\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003eOR, 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003eOR, 95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\n \u003cp\u003ep value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eNo sleep medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eBZD and/or ZD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e2.01, 1.34 - 3.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e1.66, 1.07 - 2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eNo sleep medication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20.8517%;\"\u003e\n \u003cp\u003ereference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eBZD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e1.52, 0.91 - 2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e1.13, 0.65 - 1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.670\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eZD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e2.47, 1.18 - 5.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e2.63, 1.21 - 5.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 33.3333%;\"\u003e\n \u003cp\u003eBZD and ZD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e6.55, 1.75 - 24.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.8517%;\"\u003e\n \u003cp\u003e4.66, 1.20 - 18.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4816%;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"benzodiazepine, Z-drug, mild cognitive impairment, dementia, sleep disturbance, older adults","lastPublishedDoi":"10.21203/rs.3.rs-5283552/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5283552/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe prevalence of dementia has increased in recent years, and sleep disorders are common among older adults. The purpose of this study was to clarify the association between sleep medication and cognitive function in older adults. Community-dwelling older adults were evaluated face-to-face for cognitive function and classified into normal, mild cognitive impairment, and dementia groups. Their history of sleep medication, including benzodiazepines (BZDs), Z-drugs (ZDs), and other medications, was also collected through personal interviews. Statistical analyses using trend analysis and binomial logistic regression analysis with two covariate models were performed to investigate the association between sleep medication and cognitive decline. A total of 869 participants were enrolled, and 12.5% of them were taking sleep medication. Trend analysis showed a significant association between BZD and/or ZD use and cognitive impairment (p\u0026thinsp;=\u0026thinsp;0.003). Binary logistic regression analysis with multivariate adjustment showed that BZD and/or ZD users had 1.66 times higher odds ratio of cognitive decline compared with non-users (95% confidence interval: 1.07\u0026ndash;2.56, p\u0026thinsp;=\u0026thinsp;0.023). This study demonstrated that sleep medication is associated with a higher risk of cognitive decline in community-dwelling older adults. The findings are important to advance cognitive healthcare management for older adults.\u003c/p\u003e","manuscriptTitle":"Sleep medication and risk of cognitive decline in community-dwelling older adults: The YAHABA study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-11 06:34:19","doi":"10.21203/rs.3.rs-5283552/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":"a5b67f22-4ee2-4ec2-9c01-373a093c2271","owner":[],"postedDate":"November 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39770515,"name":"Biological sciences/Neuroscience"},{"id":39770516,"name":"Health sciences/Neurology"}],"tags":[],"updatedAt":"2025-02-17T13:54:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-11 06:34:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5283552","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5283552","identity":"rs-5283552","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}