Glymphatic dysfunction and choroid plexus volume increase in older adults with poor sleep quality

Glymphatic dysfunction and choroid plexus volume increase in older adults with poor sleep quality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Glymphatic dysfunction and choroid plexus volume increase in older adults with poor sleep quality Junko Kikuta, Koji Kamagata, Kaito Takabayashi, Yayoi Hayakawa, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4244404/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Objective This study aimed to explore alterations in diffusion tensor image analysis along the perivascular space (DTI-ALPS) method and choroid plexus volume (CPV), which could be biomarkers of the glymphatic system in older adults with poor sleep quality (PSQ). Methods Fifty-two Japanese older adults with Pittsburgh Sleep Quality Index (PSQI) scores > 5 (22 men and 30 women; mean age ± SD = 73.10 ± 5.67 years) and 52 healthy controls (HCs; PSQI score ≤ 5) were included. Diffusion-weighted imaging (DWI) and 3D T1-weighted images were obtained using 3T magnetic resonance imaging. The ALPS index was calculated using preprocessed diffusion-weighted imaging (DWI), and the CPV was calculated using FreeSurfer 6.0. The mean ALPS index was subsequently compared between the PSQ group and HCs by using a general linear model (GLM) adjusted for covariates, including age, sex, years of education, intracranial volume, systolic blood pressure, diastolic blood pressure, hemoglobin A1c, and white matter lesion volume (WMLV). The CPV was also compared between the two groups by using the GLM, adjusting for the same covariates mentioned above. Next, we conducted a partial correlation analysis between the ALPS index and the CPV, Montreal Cognitive Assessment (MoCA), and PSQI scores, adjusting for all the abovementioned covariates. Results Compared with HCs, patients in the PSQ group had a significantly lower mean ALPS ( p = 0.04, Cohen’s d = − 0.28) and a greater CPV ( p = 0.11, Cohen’s d = 0.24). In the PSQ group, the mean ALPS score was significantly negatively correlated with the CPV ( r = − 0.35, false discovery rate [FDR]-corrected p = 0.03) but was significantly positively correlated with the MoCA score ( r = 0.35, FDR-corrected p = 0.03). Conclusion Older adults with PSQ exhibited a decrease in the glymphatic system and an increase in CPV. Neurobiology of Disease Cellular & Molecular Neuroscience poor sleep quality glymphatic system diffusion tensor imaging analysis along the perivascular space diffusion-weighted image cerebrospinal fluid interstitial fluid amyloid β choroid plexus volume Figures Figure 1 Figure 2 Figure 3 1 Introduction Sleep–wake homeostasis is essential for maintaining biological functions. Human sleep generally alternates between dream-filled “rapid eye movement (REM) sleep” and brain-resting “non-REM sleep” in approximately 90 min cycles, gradually preparing to start up toward morning awakening (Moszczynski and Murray, 2012 ). Many people need to sleep at almost the same time each day and wake up after approximately 7–8 hours. In modern society, sleep disorders caused by brain function disruptions have many causes, such as lifestyle habits, the environment, drug side effects, and old age. The incidence of difficulty falling asleep was not significantly different between young and advanced-aged individuals, but the incidence of waking up in the middle of the night and early in the morning was significantly greater among older adults. According to the glymphatic system hypothesis, waste products in the brain are expelled by the transport of substances via cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange (Iliff et al., 2012 ) (Fig. 1 ). CSF passes through the perivascular space around the arteries and enters the interstitial space through water channels controlled by aquaporin 4 expressed in astrocyte foot processes. In the interstitial space, CSF and ISF are exchanged as a result of bulk flow, which is hypothesized to drain the brain via the perivascular space around the veins (Mestre et al., 2018 ). This excretion mechanism eliminates the amyloid β (Aβ) and tau proteins that accumulate in the brain. CSF and ISF flow are attracting attention as concepts called CSF–ISF dynamics (Taoka, 2021 ). Since the glymphatic system hypothesis was proposed, many studies have attempted to visualize fluid dynamics within the central nervous system (Iliff et al., 2012 ; Keil et al., 2022 ). In particular, tracer studies involving the intravenous administration of gadolinium contrast agents for analyzing the human glymphatic system have become prevalent (Naganawa et al., 2017 ). Diffusion magnetic resonance imaging (MRI), which can be completed in a safe and short time, has also been employed (Taoka, 2021 ). Taoka et al. ( 2017 ) proposed the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method to evaluate the movement of water molecules toward the perivascular space by measuring diffusion coefficients. Notably, the ALPS index could reflect the impairment of ISF dynamics in the interstitial space (Taoka, 2021 ). They reported a significantly positive correlation between the ALPS index and cognitive function score measured by the Mini-Mental State Examination (MMSE) in patients with Alzheimer’s disease (AD). Subsequently, Zhang et al. ( 2021 ) used the intrathecal administration of gadolinium in patients with small-vessel disease to investigate whether the ALPS index significantly correlated with glymphatic clearance ( r = − 0.77 to − 0.84, p < 0.001), indicating that the ALPS index can reflect part of the glymphatic system. The ALPS index has been used for assessing different conditions, such as AD (Taoka et al., 2017 ; Kamagata et al., 2022 ), idiopathic normal pressure hydrocephalus (Yokota et al., 2019 ; Kikuta et al., 2022 ), diabetes (Yang et al., 2020 ), hypertension (Kikuta et al., 2021 ), and Parkinson’s disease (PD) (Ma et al., 2021 ). The glymphatic system is most active during sleep, particularly during non-REM sleep (N3) (Xie et al., 2013 ; Yan et al., 2021 ). Astrocytes in the brain may contribute to the enhancement of clearance in the brain by shrinking the cellular volume of the brain parenchyma during sleep, thereby increasing the ISF space from 14% during wakefulness to 23% during sleep (Abbott et al., 2018 ). Using a serial intravenous contrast-enhanced T1 mapping technique, Lee et al. (2021) demonstrated that sleep was associated with greater glymphatic clearance than was wakefulness. Eide et al. ( 2022 ) reported that people with sleep disorders have an increased accumulation of CSF tracers in the cerebral cortex compared with healthy controls (HCs), suggesting that the glymphatic system declines in sleep disorders. In young adults with sleep disorders, the ALPS index significantly decreased compared with that in HCs (Saito et al., 2023 ). Furthermore, the ALPS index was significantly associated with N2 sleep duration and the apnea–hypopnea index in older adults (Siow et al., 2022 ). However, glymphatic function has not yet been sufficiently compared between older adults with poor sleep quality (PSQ) diagnosed by the Pittsburgh Sleep Quality Index (PSQI) and HCs using the DTI-ALPS method. CSF flow is one of the driving forces behind the glymphatic system. The choroid plexus (CP) produces and secretes CSF and has also attracted increased interest as a biomarker of the glymphatic system. The epithelial cells of the CP have CSF–blood–brain barriers and are involved in peripheral–central immune surveillance (Erickson and Banks., 2019). CSF–blood–brain barriers mediate the transport of selected plasma proteins from the blood into the CSF. The CP volume (CPV) could be related to these CP functions; although CPV increases with age, CSF production and permeability decrease (Redzic et al., 2005 ). The CPV is reportedly increased in patients with neurodegenerative diseases such as AD (Choi et al., 2022 ), chronic pain (Zhou et al., 2015 ), schizophrenia (Zhou et al., 2020 ), stroke (Egorova et al., 2019 ), and fibromyalgia (Tu et al., 2023 ). An increase in the CPVV is also associated with impaired excretion of waste products such as Aβ (Sayedhedayatollah et al., 2018 ). CP function may be impaired in these diseases. However, the relationship between CP function and the glymphatic system has not been fully investigated. Considering these findings, we hypothesized that older adults with PSQ have an impaired glymphatic system and that using both the DTI-ALPS method and CPV could yield novel findings regarding the glymphatic system and CP function in sleep disorders. Hence, this study aimed to explore the function of the glymphatic system in older adults with PSQ by using the ALPS index and CPV. 2 Materials and Methods 2.1 Study participants This cross-sectional study included older Japanese adults (age range, 65–82 years) who were living in urban areas and had participated in the Bunkyo Health Study between March 2017 and September 2018 (Someya et al., 2019 ). The study protocol was approved by the Ethics Committee of Juntendo University and conformed to the principles of the Declaration of Helsinki. All the subjects provided written informed consent before participation (Someya et al., 2019 ). The exclusion criteria included major psychiatric or neurological disorders; heart failure; stroke; and/or history of alcohol or drug abuse. All participants were right-handed and had no history of diabetes (hemoglobin [Hb] A1c < 6.5%), hypertension (systolic blood pressure [SBP]/diastolic blood pressure [DBP] < 140/90 mmHg), hyperlipidemia (total cholesterol < 280 mg/dL), low-density lipoprotein [LDL] 40 mg/dL, or triglyceride < 150 mg/dL]) (Esumi et al., 2000 ), or obesity (body mass index [BMI] < 25 kg/m 2 ). Furthermore, we used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality, defined as an individual’s subjective satisfaction with various aspects of sleep. The PSQI ranges from 0 (best) to 21 (worst) (Buysse et al., 1989 ). The scores for each component of the PSQI ranged from 0 (best) to 3 (worst). A score of 6 was used as the cutoff point (Mollayeva et al., 2016 ). A total of 52 participants with a PSQI score of 6 or more were categorized into the PSQ group. Moreover, we categorized 52 age- and sex-matched participants with PSQI scores less than 6 as HCs. Figure 1 summarizes the study participants’ demographic characteristics. 2.2 Image acquisition We obtained 3D T1-weighted images by using magnetization-prepared 180° radiofrequency pulses and rapid gradient echo with the following parameters: repetition time (TR) = 2300 ms, echo time (TE) = 2.32 ms, inversion time (TI) = 900 ms, field of view (FOV) = 240 mm × 240 mm, matrix size = 256 × 256, resolution = 0.9 mm × 0.9 mm, slice thickness = 0.9 mm, and acquisition time = 5.21 min on a 3T MRI scanner (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany). Diffusion-weighted imaging (DWI) data were also acquired with a 64-channel head coil. Echo-planar images were acquired using a b-value of 1000 s/mm 2 along 64 isotropic diffusion gradients in the anterior–posterior phase-encoding direction with the following parameters: TR = 3300 ms; TE = 70 ms; FOV = 229 mm × 229 mm; matrix size = 130 × 130; resolution = 1.8 mm × 1.8 mm; slice thickness = 1.8 mm; and acquisition time = 7.29 min. Each DWI acquisition was completed with a b = 0 image. We obtained standard and reversed-phase-encoded blipped images with no diffusion weighting (blip-up or blip-down) to correct for magnetic susceptibility-induced distortions related to echo-planar imaging acquisitions. 2.3 White matter lesion volume measurement An experienced neuroradiologist evaluated deep white matter hyperintensity by using the Fazekas scale (Fazekas et al., 1987) according to axial fluid-attenuated inversion recovery (FLAIR) images. The white matter lesion volume (WMLV) was computed using 3D T1-weighted imaging and the Computational Anatomy Toolbox 12 ( http://www.neuro.uni-jena.de/cat ) (Gaser et al., 2022 ). 2.4 DWI preprocessing DWI data were processed using the EDDY and TOPUP toolboxes from the FMRIB Software Library version 5.0.10 (FSL; Oxford Center for Functional MRI of the Brain, Oxford, UK; www.fmrib.ox.ac.uk/fsl ) for the correction of magnetization (Yamada et al., 2014 ), eddy current distortions, and movement (Andersson and Sotiropoulos, 2016 ). The maps of fractional anisotropy (FA) and color-coded FA from preprocessed DWI data were then created using the DTIFIT tool of FSL. The diffusivity maps of each participant were taken in the directions of the x- (right–left; Dxx ), y- (anterior–posterior; Dyy ), and z-axes (inferior–superior; Dzz ). 2.5 ALPS index calculation The FA maps of all participants were also generated and registered to the FMRIB58_FA standard space ( https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FMRIB58_FA ) using the FSL linear image registration tool (FLIRT; http://www.fmrib.ox.uk/fsl/fslwiki/FLIRT ) and the nonlinear registration tool (FNIRT; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FNIRT ). For ROI placement, a 68-year-old female control participant with minimal head movement was selected as the best target. The participant with the smallest movement (i.e., closest to the standard brain) was used, as it was the best target to prevent the ROI from becoming extremely small or mistransformed as much as possible in the registration steps of the ROI to each participant’s native space (Kikuta et al., 2021 ; Kamagata et al. 2022 ). Next, using this participant’s color-coded FA map, we manually placed 5-mm-diameter square ROIs in the projection and association areas at the level of the ventricle bodies of the left and right hemispheres. The resulting ROIs were then registered to the same FA template. Subsequently, the deformation field was used to transform the ROI defined by the best target to each participant. Finally, based on each participant’s color-coded FA map, we confirmed that ROI placement had no issues. On the ROIs, Dxx ( Dxxproj) and Dyy ( Dyyproj ) of the projection fiber and Dxx ( Dxxassoc ) and Dzz ( Dzzassoc ) of the association fiber were measured. We then calculated the ALPS indices for both the left and right sides using the following formula: $$ALPS index = \frac{ mean(Dxxproj, Dxxassoc)}{ mean(Dyyproj, Dzzassoc)}$$ An ALPS index approaching 1 indicates a decrease in water diffusivity in the perivascular space (Taoka et al., 2017 ). We subsequently obtained the average of the left and right sides as the mean ALPS index. 2.6 CPV calculation We automatically estimated the structural volumes using FreeSurfer version 6.0. ( http://surfer.nmr.mgh.harvard.edu/ ). The preprocessing steps, which are based on common information from the within-subject template, include skull stripping, Talairach transforms, atlas registration, and the creation of spherical surface maps and parcellations. The intracranial volume (ICV) of each participant was obtained through this preprocessing procedure. In addition, volumetric measurements were extracted from the CP ROIs. This method has been used in several previous studies and yields reliable CP segmentation results (Zhou et al., 2015 ; Zhou et al., 2020 ). All the CP ROIs were comprehensively examined and corrected manually when needed. Next, the CPVs on the left and right sides were summed. 2.7 Statistical analyses All the statistical data were analyzed using IBM SPSS for Windows 23.0 (IBM Corporation, Armonk, NY, USA). The ALPS index reportedly decreases with hypertension and diabetes (Yang et al., 2020 ; Kikuta et al., 2021 ). In addition, the participants were older adults and had slight white matter lesions, although these lesions did not exist in the ROI. Therefore, in addition to age, sex, years of education, and ICV, we included SBP, HbA1c, and the WMLV as covariates. Overall, the mean ALPS indices were compared between the PSQ group and HCs using a general linear model (GLM), adjusting for covariates such as age, sex, years of education, ICV, SBP, DBP, HbA1c, and WMLV. The CPV was also compared between such groups using the GLM and adjusting for the same covariates. Moreover, the effect sizes were calculated using Cohen’s d to evaluate the statistical power of the relationships according to the group comparisons. Effect sizes of 0.2, 0.5, and 0.8 were categorized as small, medium, and large, respectively (Cohen, 1992 ). The significance level was set at 5%. Next, we conducted a partial correlation analysis between the ALPS index and the CPV, MoCA, and PSQI scores in the PSQ group and HCs adjusted for the same covariates. Partial correlation analyses between the mean ALPS index/CPV and each PSQI component in all participants were performed using sex, age, education year, SBP, DBP, Hb1Ac, ICV, and WMLV as covariates. Multiple comparisons were corrected using the false discovery rate (FDR) procedure (Benjamini and Hochberg, 1995 ). An FDR-corrected p value less than 0.05 was considered to indicate statistical significance. In addition, multivariate linear regression analyses between the mean ALPS index/CPV and each PSQI component in the PSQ group were performed using sex, age, education year, SBP, DBP, Hb1Ac, ICV, and WMLV as covariates. 3 Results 3.1 Demographic and clinical characteristics Figure 1 summarizes the features of the PSQ group and HCs. Age, sex, education year, MMSE score, MoCA score, HbA1c, SBP, DBP, pulse rate, total cholesterol, LDL, HDL, triglyceride, BMI, periventricular hyperintensity, and deep and subcortical white matter hyperintensity did not significantly differ between the two groups ( p > 0.05). As expected, the PSQ group had a significantly greater total PSQI score for all components than did the HCs ( p < 0.001). 3.2 Group differences The mean ALPS index was significantly lower in the PSQ group than in the HCs ( p = 0.04, Cohen’s d = − 0.29) (Fig. 2 ). Additionally, the PSQ group tended to have a greater CPV than did the HCs ( p = 0.11, Cohen’s d = 0.24) (Fig. 2 ). 3.3 Correlation analyses The mean ALPS score was significantly negatively correlated with the CPV ( r = − 0.35, FDR-corrected p = 0.03) and positively correlated with the MoCA score ( r = 0.35, FDR-corrected p = 0.03) in the PSQ group (Fig. 3 ). Conversely, no significant correlations were observed in the HCs (CPV: r = − 0.12, FDR-corrected p = 0.45; MoCA: r = 0.20, FDR-corrected p = 0.32) (Fig. 3 ). The mean ALPS did not significantly associate with the PSQI score in either the PSQ group ( r = 0.24, FDR-corrected p = 0.12) or the HCs ( r = 0.01, FDR-corrected p = 0.96) (Fig. 3 ). Furthermore, no significant associations were observed between the mean ALPS index or the CPV and each PSQI component (Supplementary Tables 1 and 2). 4 Discussion This study evaluated the glymphatic system in older adults with PSQ by using the DTI-ALPS method and CPV as noninvasive tools, and the following results were obtained. First, the PSQ group had a significantly lower mean ALPS index than did the HCs. Second, the mean CPV of the PSQ group tended to be greater than that of the HCs. Third, the mean ALPS score was significantly negatively correlated with the CPV in the PSQ group. Fourth, these indices had significantly positive correlations with MoCA scores in the PSQ group. The glymphatic system is active during sleep homeostasis (Xie et al., 2013 ). Sleep disorders can reduce glymphatic function, causing Aβ accumulation in the brain. In mice, even a single night of sleep deprivation can increase Aβ levels in the brain, causing a greater amyloid burden in brain regions, including the hippocampus and thalamus (Ooms et al., 2014 ). Compared with healthy individuals, people with sleep disorders exhibit stagnation in the aggregation of CSF tracers in the brain, indicating a decrease in excretory function (Eide et al., 2022 ). Notably, in healthy human brains, the uptake of amyloid tracers occurs in the cerebral white matter, but in severe obstructive sleep apnea, this uptake increases because of a decrease in glymphatic function (Ylä-Herttuala et al., 2022 ). Furthermore, Lee et al. ( 2021b ) showed a significant negative correlation between the ALPS index and the apnea–hypopnea index, suggesting that the glymphatic system is impaired in obstructive sleep apnea. In addition, the ALPS index was significantly lower in patients with isolated REM sleep behavior disorder than in HCs (Lee et al., 2021c ). In young adults, the ALPS index significantly decreased compared with that in HCs (Saito et al., 2023 ). Our current findings showed that the ALPS index was significantly lower in Japanese older adults with PSQ than in HCs, consistent with the results of previous reports related to sleep disorders. Overall, interstitial fluidopathy might occur in older adults with PSQ. The present study also demonstrated that the ALPS score was significantly associated with cognitive function in Japanese older adults with PSQ. The ALPS index is reportedly significantly related to cognitive function in older adults (Steward et al., 2021 ), AD (Taoka et al., 2017 ), and PD (Ma et al., 2021 ). Steward et al. ( 2021 ) reported significant correlations between the ALPS index and the MMSE score in older adults. Likewise, Taoka et al. ( 2017 ) revealed a significantly positive correlation between the ALPS index and the MMSE score, indicating a lower ALPS index in relation to the degree of AD severity. Ma et al. ( 2021 ) also showed a significant positive correlation between the ALPS index and the MMSE score in the early PD group ( β = 0.021, p = 0.029). Therefore, the glymphatic system may be related to cognitive function. The obstruction of glymphatic flow and the resultant accumulation of cytokines and metabolic wastes, including Aβ, might form a vicious cycle that perpetuates neuroinflammation (Mogensen et al., 2021 ). Neuroinflammation could further exacerbate waste product accumulation in interstitial and perivascular spaces. Eventually, this negative cycle can cause cognitive decline (Mogensen et al., 2021 ). This study indicated that the ALPS index, which reflects the impairment of ISF dynamics in the interstitial space, decreased and was related to cognitive function in the PSQ group. Thus, pathological backgrounds similar to those of AD patients have been reinforced in older adults with PSQ compared with those in HCs. In this study, CPV tended to increase in Japanese older adults with PSQ. Notably, the ALPS index was significantly negatively correlated with CPV in older adults with PSQ. The CP is a key regulator of CSF production and plays an important role in neurodegenerative, neuroinflammatory, and neuropsychological diseases (Municio et al., 2023 ). The CP epithelium, which comprises the CSF–blood–brain barrier, has a great immunological brain interface. Alterations in the CSF–blood–brain barrier system with age could change the aspects of protective adaptive immunity in the central nervous system (Erickson and Banks, 2019). Several Aβ transporters, including LRP1, LRP2, RAGE, and AΒCB1, which are generally expressed at the blood–brain barrier, have also been found in the CP epithelium on the CSF side (Crossgrove et al., 2005 ). Fujiyoshi et al. ( 2011 ) reported that radiolabeled Aβ particles injected into lateral ventricles of rats were collected via the CP and subsequently eliminated from the CSF fivefold faster than if they were removed via CSF bulk flow alone. Therefore, CP may capture and remove proteins such as Aβ in the CSF, indicating that CP function is involved in clearance. CPV was negatively associated with CSF protein levels in healthy and diseased individuals, which could indicate that CP enlargement is linked to dysfunction in CSF production and/or clearance (Sayedhedayatollah et al., 2018 ; Tadayon et al., 2020). A higher CPV was correlated with a slower glymphatic clearance rate in all brain locations (Li et al., 2020 ), and an enlarged CPV could be an indicator of greater growth of the WML, potentially involving impaired glymphatic function (Li et al., 2023 ). Furthermore, Tu et al. ( 2023 ) found a negative correlation between CPV and the ALPS index in patients with fibromyalgia ( r = − 0.522, p = 0.01). These findings suggest that CP enlargement is associated with glymphatic dysfunction. The findings in older adults with PSQ are consistent with those of previous reports. Therefore, the CP concentration tends to increase during glymphatic dysfunction caused by various conditions, including PSQ (Johnson et al., 2020 ). This study has several limitations. First, it was a single cohort study. However, large multicenter studies are needed to validate our results. Second, this study included only Japanese older adults. Therefore, this research should also be conducted in other races. Third, considering the use of the FreeSurfer technique, the CPV of the lateral ventricle, which is the main CP, was used in the analysis without the third or fourth ventricle. Fourth, there are only a few reports on CPV. In addition, the participants in this study were older adults, and the sample size was small. Thus, it was challenging to determine the factors for inclusion as covariates. In this study, sex, age, education year, SBP, DBP, MoCA score, ICV, and WMLV, which affect the indicators of the glymphatic system, including the ALPS index, were included as covariates in the analysis of CPV incidence. However, the factors that affect CPV in older adults must be further investigated. Finally, similar to the findings of previous studies, this study placed the ROIs at the level of the corona radiata and calculated the ALPS index, thereby allowing us to evaluate the water diffusion ability of perivascular spaces only in a part of the brain. 5 Conclusion Older adults with PSQ exhibited a decrease in the glymphatic system and an increase in CPV. Declarations Ethics Statement This study was approved by the Ethics Committee of Juntendo University. All study participants or their legal guardians were briefed on the details of this study and provided written informed consent. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest. Author Contributions JK conceived and designed the analysis, analyzed and interpreted the data, and drafted the manuscript. KK and KT performed the data acquisition and analyzed and interpreted the data. All the authors have read and revised this manuscript and approved the final manuscript. Funding This work was supported by JSPS KAKENHI (Grant Numbers 20K16737 and 21K15833); the MEXT-Supported Program for the Strategic Research Foundation at Private Universities; 2014–2018 (S1411006) from the Ministry of Education, Culture, Sports, Science and Technology of Japan; and the Otsuka Toshimi Scholarship Foundation to SG. Acknowledgments The authors would like to thank Enago (www.enago.jp) for the English language review. 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Skeletal muscle function and need for long-term care of urban elderly people in Japan (the Bunkyo Health Study): a prospective cohort study. BMJ Open 9(9) , e031584. doi: 10.1136/bmjopen-2019-031584. Steward CE, VenkatramanVK, Lui E, Malpas CBC, EllisKA, Cyarto EV, et al. (2021). Assessment of the DTI-ALPS Parameter along the perivascular space in older adults at risk of dementia J Neuroimaging . doi: 10.1111/jon.12837. Tadayon E, Pascual-Leone A, Press D, Santarnecchi E. Choroid plexus volume is associated with levels of CSF proteins: relevance for Alzheimer's and Parkinson's disease. Neurobiol Aging 2020, 89, 108-117. doi: 10.1016/j.neurobiolaging.2020.01.005 Taoka, T. (2021). Neurofluid as assessed by diffusion-weighted imaging Magn Reson Imaging Clin N Am 29(2), 243-251. doi: 10.1016/j.mric.2021.01.002. Taoka T, MasutaniY, Kawai H, Nakane T, MatsuokaK, Yasuno F, et al. (2017). Evaluation of glymphatic system activity using the diffusion MR technique: diffusion tensor image analysis along the perivascular space (DTI-ALPS) in patients with Alzheimer’s disease Jpn J Radiol 35(4) , 172-178. doi: 10.1007/s11604-017-0617-z. Tu, Y., Li, Z., Xiong, F., and Gao, F. (2023). Decreased DTI-ALPS and choroid plexus enlargement in fibromyalgia: a preliminary multimodal MRI study. Neuroradiology . doi: 10.1007/s00234-023-03240-8. Xie L, KangH, Xu Q, Chen MJ, LiaoY, Thiyagarajan M, et al. (2013). Sleep drives metabolite clearance from the adult brain. Science 342(6156) , 373-377. doi: 10.1126/science.1241224. Yamada H, AbeO, Shizukuishi T, Kikuta J, ShinozakiT, Dezawa K, et al. (2014). Efficacy of distortion correction on diffusion imaging: comparison of FSL eddy and Eddy_correct using 30 and 60 Directions Diffusion Encoding. PLoS ONE 9(11) , e112411. doi: 10.1371/journal.pone.0112411. Yan, T., Qiu, Y., Yu, X., and Yang, L. (2021). Glymphatic Dysfunction: A Bridge Between Sleep Disturbance and Mood Disorders. Frontiers in Psychiatry 12. doi: 10.3389/fpsyt.2021.658340. Yang, G., Deng, N., Liu, Y., Gu, Y., and Yao, X. (2020). Evaluation of the Glymphatic System Using Diffusion MR Technique in T2DM Cases. Front Hum Neurosci 14 , 300. doi: 10.3389/fnhum.2020.00300. Ylä-Herttuala S, HakulinenM, Poutiainen P, Lötjönen J, KönönenM, Gröhn H, et al. (2022). Decreased Gray– White Matter Contrast of [11C]-PiB Uptake in Cognitively Unimpaired Subjects with Severe Obstructive Sleep Apnea. J Prev Alzheimers Dis 9(3) , 499-506. doi: 10.14283/jpad.2022.24. Yokota H, Vijayasarathi A, Cekic M, Hirata Y, LinetskyM, Ho M, et al. (2019). Diagnostic Performance of Glymphatic System Evaluation Using Diffusion Tensor Imaging in Patients with Idiopathic Normal Pressure Hydrocephalus and Mimickers. Curr Gerontol Geriatr Res 2019 , 5675014. doi: 10.1155/2019/5675014. Zhang W, ZhouY, Wang J, Gong X, ChenZ, Zhang X, et al. (2021). Glymphatic clearance function in patients with cerebral small vessel disease. NeuroImage 238 , 118257. doi: https://doi.org/10.1016/j.neuroimage.2021.118257. Zhou, G., Hotta, J., Lehtinen, M.K., Forss, N., and Hari, R. (2015). Enlargement of the choroid plexus in complex regional pain syndrome. Sci Rep 5 , 14329. doi: 10.1038/srep14329. Zhou, Y.F., Huang, J.C., Zhang, P., Fan, F.M., Chen, S., Fan, H.Z., et al. (2020). Choroid Plexus Enlargement and Allostatic Loading in Schizophrenia. Schizophrenia Bulletin 46(3) , 722–731. doi: 10.1093/schbul/sbz100. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterial.docx Supplementary Materials Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4244404","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":289976104,"identity":"58ab6190-ee4f-4601-82df-4a2726eb3399","order_by":0,"name":"Junko 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Juntendo University Graduate School of Medicine, Tokyo, Japan","correspondingAuthor":false,"prefix":"","firstName":"Yoshifumi","middleName":"","lastName":"Tamura","suffix":""},{"id":289981631,"identity":"777af1ca-488c-466f-b44b-1f432dafd5a9","order_by":13,"name":"Ryuzo Kawamori","email":"","orcid":"","institution":"Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan","correspondingAuthor":false,"prefix":"","firstName":"Ryuzo","middleName":"","lastName":"Kawamori","suffix":""},{"id":289981632,"identity":"5090e249-afbe-4fb9-9620-f131ce0985d0","order_by":14,"name":"Hirotaka Watada","email":"","orcid":"","institution":"Department of Metabolism and Endocrinology, Juntendo University Graduate School of Medicine, Tokyo, Japan","correspondingAuthor":false,"prefix":"","firstName":"Hirotaka","middleName":"","lastName":"Watada","suffix":""},{"id":289981633,"identity":"cd49b6ca-dd47-4747-aeb9-ab83d321d4d3","order_by":15,"name":"Shigeki Aoki","email":"","orcid":"","institution":"Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan","correspondingAuthor":false,"prefix":"","firstName":"Shigeki","middleName":"","lastName":"Aoki","suffix":""}],"badges":[],"createdAt":"2024-04-10 01:19:57","currentVersionCode":2,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4244404/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-4244404/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54510824,"identity":"696b1176-ad04-4a8d-8dfd-737fa1755ac7","added_by":"auto","created_at":"2024-04-11 15:27:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140974,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePatient demographic characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic and clinical data were analyzed using the chi-square test or Mann–Whitney U test. MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; SBP, systolic blood pressure; DBP, diastolic blood pressure; HbA1c, hemoglobin A1c; T-Cho, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; PVH, periventricular hyperintensity; DSWMH, deep and subcortical white matter hyperintensity.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4244404/v2/148eab8d273c1d0f9e278031.jpg"},{"id":54510827,"identity":"672cc08e-c136-43e5-b3a2-c382555a7b9b","added_by":"auto","created_at":"2024-04-11 15:27:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45654,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of the mean ALPS index and CPV between the PSQ group and HCs.\u003c/strong\u003e ALPS, along the perivascular space; CPV, choroid plexus volume; PSQ, poor sleep quality; HCs, healthy controls.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4244404/v2/a3c1d9689b28195fc59d8d72.jpg"},{"id":54510826,"identity":"039c343f-eba5-4d20-9644-66b1946c62c3","added_by":"auto","created_at":"2024-04-11 15:27:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":102146,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelations of the mean ALPS index with the CPV score, MoCA score, and PSQI score in the PSQ group and HCs.\u003c/strong\u003e CPV, choroid plexus volume; PSQ, poor sleep quality; HCs, healthy controls; MoCA, Montreal Cognitive Assessment; PSQI, Pittsburgh Sleep Quality Index. FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4244404/v2/ba2dad8a0e1eb56d344fff7f.jpg"},{"id":54511449,"identity":"97ff3e85-46b3-4e29-9a2c-ede921425071","added_by":"auto","created_at":"2024-04-11 15:35:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":522585,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4244404/v2/a0f059a9-bd63-43c6-92cd-8e0a58a19c81.pdf"},{"id":54510825,"identity":"60c47816-4fd9-4115-9f4e-04541baed6b0","added_by":"auto","created_at":"2024-04-11 15:27:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30916,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Materials\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4244404/v2/43195a7c816ece97719152c2.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Glymphatic dysfunction and choroid plexus volume increase in older adults with poor sleep quality","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSleep\u0026ndash;wake homeostasis is essential for maintaining biological functions. Human sleep generally alternates between dream-filled \u0026ldquo;rapid eye movement (REM) sleep\u0026rdquo; and brain-resting \u0026ldquo;non-REM sleep\u0026rdquo; in approximately 90 min cycles, gradually preparing to start up toward morning awakening (Moszczynski and Murray, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Many people need to sleep at almost the same time each day and wake up after approximately 7\u0026ndash;8 hours. In modern society, sleep disorders caused by brain function disruptions have many causes, such as lifestyle habits, the environment, drug side effects, and old age. The incidence of difficulty falling asleep was not significantly different between young and advanced-aged individuals, but the incidence of waking up in the middle of the night and early in the morning was significantly greater among older adults.\u003c/p\u003e \u003cp\u003eAccording to the glymphatic system hypothesis, waste products in the brain are expelled by the transport of substances via cerebrospinal fluid (CSF) and interstitial fluid (ISF) exchange (Iliff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). CSF passes through the perivascular space around the arteries and enters the interstitial space through water channels controlled by aquaporin 4 expressed in astrocyte foot processes. In the interstitial space, CSF and ISF are exchanged as a result of bulk flow, which is hypothesized to drain the brain via the perivascular space around the veins (Mestre et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This excretion mechanism eliminates the amyloid β (Aβ) and tau proteins that accumulate in the brain. CSF and ISF flow are attracting attention as concepts called CSF\u0026ndash;ISF dynamics (Taoka, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSince the glymphatic system hypothesis was proposed, many studies have attempted to visualize fluid dynamics within the central nervous system (Iliff et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Keil et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In particular, tracer studies involving the intravenous administration of gadolinium contrast agents for analyzing the human glymphatic system have become prevalent (Naganawa et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Diffusion magnetic resonance imaging (MRI), which can be completed in a safe and short time, has also been employed (Taoka, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Taoka et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) proposed the diffusion tensor image analysis along the perivascular space (DTI-ALPS) method to evaluate the movement of water molecules toward the perivascular space by measuring diffusion coefficients. Notably, the ALPS index could reflect the impairment of ISF dynamics in the interstitial space (Taoka, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). They reported a significantly positive correlation between the ALPS index and cognitive function score measured by the Mini-Mental State Examination (MMSE) in patients with Alzheimer\u0026rsquo;s disease (AD). Subsequently, Zhang et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used the intrathecal administration of gadolinium in patients with small-vessel disease to investigate whether the ALPS index significantly correlated with glymphatic clearance (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.77 to \u0026minus;\u0026thinsp;0.84, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that the ALPS index can reflect part of the glymphatic system. The ALPS index has been used for assessing different conditions, such as AD (Taoka et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kamagata et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), idiopathic normal pressure hydrocephalus (Yokota et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kikuta et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), diabetes (Yang et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), hypertension (Kikuta et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Parkinson\u0026rsquo;s disease (PD) (Ma et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe glymphatic system is most active during sleep, particularly during non-REM sleep (N3) (Xie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Yan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Astrocytes in the brain may contribute to the enhancement of clearance in the brain by shrinking the cellular volume of the brain parenchyma during sleep, thereby increasing the ISF space from 14% during wakefulness to 23% during sleep (Abbott et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Using a serial intravenous contrast-enhanced T1 mapping technique, Lee et al. (2021) demonstrated that sleep was associated with greater glymphatic clearance than was wakefulness. Eide et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported that people with sleep disorders have an increased accumulation of CSF tracers in the cerebral cortex compared with healthy controls (HCs), suggesting that the glymphatic system declines in sleep disorders. In young adults with sleep disorders, the ALPS index significantly decreased compared with that in HCs (Saito et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, the ALPS index was significantly associated with N2 sleep duration and the apnea\u0026ndash;hypopnea index in older adults (Siow et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, glymphatic function has not yet been sufficiently compared between older adults with poor sleep quality (PSQ) diagnosed by the Pittsburgh Sleep Quality Index (PSQI) and HCs using the DTI-ALPS method.\u003c/p\u003e \u003cp\u003eCSF flow is one of the driving forces behind the glymphatic system. The choroid plexus (CP) produces and secretes CSF and has also attracted increased interest as a biomarker of the glymphatic system. The epithelial cells of the CP have CSF\u0026ndash;blood\u0026ndash;brain barriers and are involved in peripheral\u0026ndash;central immune surveillance (Erickson and Banks., 2019). CSF\u0026ndash;blood\u0026ndash;brain barriers mediate the transport of selected plasma proteins from the blood into the CSF. The CP volume (CPV) could be related to these CP functions; although CPV increases with age, CSF production and permeability decrease (Redzic et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The CPV is reportedly increased in patients with neurodegenerative diseases such as AD (Choi et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), chronic pain (Zhou et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), schizophrenia (Zhou et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), stroke (Egorova et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and fibromyalgia (Tu et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An increase in the CPVV is also associated with impaired excretion of waste products such as Aβ (Sayedhedayatollah et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). CP function may be impaired in these diseases. However, the relationship between CP function and the glymphatic system has not been fully investigated.\u003c/p\u003e \u003cp\u003eConsidering these findings, we hypothesized that older adults with PSQ have an impaired glymphatic system and that using both the DTI-ALPS method and CPV could yield novel findings regarding the glymphatic system and CP function in sleep disorders. Hence, this study aimed to explore the function of the glymphatic system in older adults with PSQ by using the ALPS index and CPV.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cp\u003e2.1 Study participants\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study included older Japanese adults (age range, 65\u0026ndash;82 years) who were living in urban areas and had participated in the Bunkyo Health Study between March 2017 and September 2018 (Someya et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The study protocol was approved by the Ethics Committee of Juntendo University and conformed to the principles of the Declaration of Helsinki. All the subjects provided written informed consent before participation (Someya et al., \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). The exclusion criteria included major psychiatric or neurological disorders; heart failure; stroke; and/or history of alcohol or drug abuse. All participants were right-handed and had no history of diabetes (hemoglobin [Hb] A1c\u0026thinsp;\u0026lt;\u0026thinsp;6.5%), hypertension (systolic blood pressure [SBP]/diastolic blood pressure [DBP]\u0026thinsp;\u0026lt;\u0026thinsp;140/90 mmHg), hyperlipidemia (total cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;280 mg/dL), low-density lipoprotein [LDL]\u0026thinsp;\u0026lt;\u0026thinsp;190 mg/dL, high-density lipoprotein [HDL]\u0026thinsp;\u0026gt;\u0026thinsp;40 mg/dL, or triglyceride\u0026thinsp;\u0026lt;\u0026thinsp;150 mg/dL]) (Esumi et al., \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e), or obesity (body mass index [BMI]\u0026thinsp;\u0026lt;\u0026thinsp;25 kg/m\u003csup\u003e2\u003c/sup\u003e). Furthermore, we used the Pittsburgh Sleep Quality Index (PSQI) to assess sleep quality, defined as an individual\u0026rsquo;s subjective satisfaction with various aspects of sleep. The PSQI ranges from 0 (best) to 21 (worst) (Buysse et al., \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e). The scores for each component of the PSQI ranged from 0 (best) to 3 (worst). A score of 6 was used as the cutoff point (Mollayeva et al., \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). A total of 52 participants with a PSQI score of 6 or more were categorized into the PSQ group. Moreover, we categorized 52 age- and sex-matched participants with PSQI scores less than 6 as HCs. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the study participants\u0026rsquo; demographic characteristics.\u003c/p\u003e\n\u003cp\u003e2.2 Image acquisition\u003c/p\u003e\n\u003cp\u003eWe obtained 3D T1-weighted images by using magnetization-prepared 180\u0026deg; radiofrequency pulses and rapid gradient echo with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;2300 ms, echo time (TE)\u0026thinsp;=\u0026thinsp;2.32 ms, inversion time (TI)\u0026thinsp;=\u0026thinsp;900 ms, field of view (FOV)\u0026thinsp;=\u0026thinsp;240 mm \u0026times; 240 mm, matrix size\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, resolution\u0026thinsp;=\u0026thinsp;0.9 mm \u0026times; 0.9 mm, slice thickness\u0026thinsp;=\u0026thinsp;0.9 mm, and acquisition time\u0026thinsp;=\u0026thinsp;5.21 min on a 3T MRI scanner (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany). Diffusion-weighted imaging (DWI) data were also acquired with a 64-channel head coil. Echo-planar images were acquired using a b-value of 1000 s/mm\u003csup\u003e2\u003c/sup\u003e along 64 isotropic diffusion gradients in the anterior\u0026ndash;posterior phase-encoding direction with the following parameters: TR\u0026thinsp;=\u0026thinsp;3300 ms; TE\u0026thinsp;=\u0026thinsp;70 ms; FOV\u0026thinsp;=\u0026thinsp;229 mm \u0026times; 229 mm; matrix size\u0026thinsp;=\u0026thinsp;130 \u0026times; 130; resolution\u0026thinsp;=\u0026thinsp;1.8 mm \u0026times; 1.8 mm; slice thickness\u0026thinsp;=\u0026thinsp;1.8 mm; and acquisition time\u0026thinsp;=\u0026thinsp;7.29 min. Each DWI acquisition was completed with a b\u0026thinsp;=\u0026thinsp;0 image. We obtained standard and reversed-phase-encoded blipped images with no diffusion weighting (blip-up or blip-down) to correct for magnetic susceptibility-induced distortions related to echo-planar imaging acquisitions.\u003c/p\u003e\n\u003cp\u003e2.3 White matter lesion volume measurement\u003c/p\u003e\n\u003cp\u003eAn experienced neuroradiologist evaluated deep white matter hyperintensity by using the Fazekas scale (Fazekas et al., 1987) according to axial fluid-attenuated inversion recovery (FLAIR) images. The white matter lesion volume (WMLV) was computed using 3D T1-weighted imaging and the Computational Anatomy Toolbox 12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neuro.uni-jena.de/cat\u003c/span\u003e\u003c/span\u003e) (Gaser et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e2.4 DWI preprocessing\u003c/p\u003e\n\u003cp\u003eDWI data were processed using the EDDY and TOPUP toolboxes from the FMRIB Software Library version 5.0.10 (FSL; Oxford Center for Functional MRI of the Brain, Oxford, UK; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.neuro.uni-jena.de/cat\" target=\"_blank\"\u003ewww.fmrib.ox.ac.uk/fsl\u003c/a\u003e\u003c/span\u003e\u003c/span\u003e) for the correction of magnetization (Yamada et al., \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), eddy current distortions, and movement (Andersson and Sotiropoulos, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The maps of fractional anisotropy (FA) and color-coded FA from preprocessed DWI data were then created using the DTIFIT tool of FSL. The diffusivity maps of each participant were taken in the directions of the x- (right\u0026ndash;left; \u003cem\u003eDxx\u003c/em\u003e), y- (anterior\u0026ndash;posterior; \u003cem\u003eDyy\u003c/em\u003e), and z-axes (inferior\u0026ndash;superior; \u003cem\u003eDzz\u003c/em\u003e).\u003c/p\u003e\n\u003cp\u003e2.5 ALPS index calculation\u003c/p\u003e\n\u003cp\u003eThe FA maps of all participants were also generated and registered to the FMRIB58_FA standard space (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FMRIB58_FA\u003c/span\u003e\u003c/span\u003e) using the FSL linear image registration tool (FLIRT; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.fmrib.ox.uk/fsl/fslwiki/FLIRT\u003c/span\u003e\u003c/span\u003e) and the nonlinear registration tool (FNIRT; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FNIRT\u003c/span\u003e\u003c/span\u003e). For ROI placement, a 68-year-old female control participant with minimal head movement was selected as the best target. The participant with the smallest movement (i.e., closest to the standard brain) was used, as it was the best target to prevent the ROI from becoming extremely small or mistransformed as much as possible in the registration steps of the ROI to each participant\u0026rsquo;s native space (Kikuta et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Kamagata et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). Next, using this participant\u0026rsquo;s color-coded FA map, we manually placed 5-mm-diameter square ROIs in the projection and association areas at the level of the ventricle bodies of the left and right hemispheres. The resulting ROIs were then registered to the same FA template. Subsequently, the deformation field was used to transform the ROI defined by the best target to each participant. Finally, based on each participant\u0026rsquo;s color-coded FA map, we confirmed that ROI placement had no issues. On the ROIs, \u003cem\u003eDxx\u003c/em\u003e (\u003cem\u003eDxxproj)\u003c/em\u003e and \u003cem\u003eDyy\u003c/em\u003e (\u003cem\u003eDyyproj\u003c/em\u003e) of the projection fiber and Dxx (\u003cem\u003eDxxassoc\u003c/em\u003e) and \u003cem\u003eDzz\u003c/em\u003e (\u003cem\u003eDzzassoc\u003c/em\u003e) of the association fiber were measured. We then calculated the ALPS indices for both the left and right sides using the following formula:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$ALPS index = \\frac{ mean(Dxxproj, Dxxassoc)}{ mean(Dyyproj, Dzzassoc)}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eAn ALPS index approaching 1 indicates a decrease in water diffusivity in the perivascular space (Taoka et al., \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). We subsequently obtained the average of the left and right sides as the mean ALPS index.\u003c/p\u003e\n\u003cp\u003e2.6 CPV calculation\u003c/p\u003e\n\u003cp\u003eWe automatically estimated the structural volumes using FreeSurfer version 6.0. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003c/span\u003e). The preprocessing steps, which are based on common information from the within-subject template, include skull stripping, Talairach transforms, atlas registration, and the creation of spherical surface maps and parcellations. The intracranial volume (ICV) of each participant was obtained through this preprocessing procedure. In addition, volumetric measurements were extracted from the CP ROIs. This method has been used in several previous studies and yields reliable CP segmentation results (Zhou et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhou et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). All the CP ROIs were comprehensively examined and corrected manually when needed. Next, the CPVs on the left and right sides were summed.\u003c/p\u003e\n\u003cp\u003e2.7 Statistical analyses\u003c/p\u003e\n\u003cp\u003eAll the statistical data were analyzed using IBM SPSS for Windows 23.0 (IBM Corporation, Armonk, NY, USA). The ALPS index reportedly decreases with hypertension and diabetes (Yang et al., \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Kikuta et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, the participants were older adults and had slight white matter lesions, although these lesions did not exist in the ROI. Therefore, in addition to age, sex, years of education, and ICV, we included SBP, HbA1c, and the WMLV as covariates. Overall, the mean ALPS indices were compared between the PSQ group and HCs using a general linear model (GLM), adjusting for covariates such as age, sex, years of education, ICV, SBP, DBP, HbA1c, and WMLV. The CPV was also compared between such groups using the GLM and adjusting for the same covariates. Moreover, the effect sizes were calculated using Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e to evaluate the statistical power of the relationships according to the group comparisons. Effect sizes of 0.2, 0.5, and 0.8 were categorized as small, medium, and large, respectively (Cohen, \u003cspan class=\"CitationRef\"\u003e1992\u003c/span\u003e). The significance level was set at 5%. Next, we conducted a partial correlation analysis between the ALPS index and the CPV, MoCA, and PSQI scores in the PSQ group and HCs adjusted for the same covariates. Partial correlation analyses between the mean ALPS index/CPV and each PSQI component in all participants were performed using sex, age, education year, SBP, DBP, Hb1Ac, ICV, and WMLV as covariates. Multiple comparisons were corrected using the false discovery rate (FDR) procedure (Benjamini and Hochberg, \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e). An FDR-corrected \u003cem\u003ep\u003c/em\u003e value less than 0.05 was considered to indicate statistical significance. In addition, multivariate linear regression analyses between the mean ALPS index/CPV and each PSQI component in the PSQ group were performed using sex, age, education year, SBP, DBP, Hb1Ac, ICV, and WMLV as covariates.\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e3.1 Demographic and clinical characteristics\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the features of the PSQ group and HCs. Age, sex, education year, MMSE score, MoCA score, HbA1c, SBP, DBP, pulse rate, total cholesterol, LDL, HDL, triglyceride, BMI, periventricular hyperintensity, and deep and subcortical white matter hyperintensity did not significantly differ between the two groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). As expected, the PSQ group had a significantly greater total PSQI score for all components than did the HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003cp\u003e3.2 Group differences\u003c/p\u003e\n\u003cp\u003eThe mean ALPS index was significantly lower in the PSQ group than in the HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.29) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Additionally, the PSQ group tended to have a greater CPV than did the HCs (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e3.3 Correlation analyses\u003c/p\u003e\n\u003cp\u003eThe mean ALPS score was significantly negatively correlated with the CPV (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.35, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and positively correlated with the MoCA score (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.35, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) in the PSQ group (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Conversely, no significant correlations were observed in the HCs (CPV: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.12, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.45; MoCA: \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The mean ALPS did not significantly associate with the PSQI score in either the PSQ group (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12) or the HCs (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Furthermore, no significant associations were observed between the mean ALPS index or the CPV and each PSQI component (Supplementary Tables\u0026nbsp;1 and 2).\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study evaluated the glymphatic system in older adults with PSQ by using the DTI-ALPS method and CPV as noninvasive tools, and the following results were obtained. First, the PSQ group had a significantly lower mean ALPS index than did the HCs. Second, the mean CPV of the PSQ group tended to be greater than that of the HCs. Third, the mean ALPS score was significantly negatively correlated with the CPV in the PSQ group. Fourth, these indices had significantly positive correlations with MoCA scores in the PSQ group.\u003c/p\u003e \u003cp\u003eThe glymphatic system is active during sleep homeostasis (Xie et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Sleep disorders can reduce glymphatic function, causing Aβ accumulation in the brain. In mice, even a single night of sleep deprivation can increase Aβ levels in the brain, causing a greater amyloid burden in brain regions, including the hippocampus and thalamus (Ooms et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Compared with healthy individuals, people with sleep disorders exhibit stagnation in the aggregation of CSF tracers in the brain, indicating a decrease in excretory function (Eide et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Notably, in healthy human brains, the uptake of amyloid tracers occurs in the cerebral white matter, but in severe obstructive sleep apnea, this uptake increases because of a decrease in glymphatic function (Yl\u0026auml;-Herttuala et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, Lee et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e) showed a significant negative correlation between the ALPS index and the apnea\u0026ndash;hypopnea index, suggesting that the glymphatic system is impaired in obstructive sleep apnea. In addition, the ALPS index was significantly lower in patients with isolated REM sleep behavior disorder than in HCs (Lee et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021c\u003c/span\u003e). In young adults, the ALPS index significantly decreased compared with that in HCs (Saito et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Our current findings showed that the ALPS index was significantly lower in Japanese older adults with PSQ than in HCs, consistent with the results of previous reports related to sleep disorders. Overall, interstitial fluidopathy might occur in older adults with PSQ.\u003c/p\u003e \u003cp\u003eThe present study also demonstrated that the ALPS score was significantly associated with cognitive function in Japanese older adults with PSQ. The ALPS index is reportedly significantly related to cognitive function in older adults (Steward et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), AD (Taoka et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and PD (Ma et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Steward et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reported significant correlations between the ALPS index and the MMSE score in older adults. Likewise, Taoka et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) revealed a significantly positive correlation between the ALPS index and the MMSE score, indicating a lower ALPS index in relation to the degree of AD severity. Ma et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also showed a significant positive correlation between the ALPS index and the MMSE score in the early PD group (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029). Therefore, the glymphatic system may be related to cognitive function. The obstruction of glymphatic flow and the resultant accumulation of cytokines and metabolic wastes, including Aβ, might form a vicious cycle that perpetuates neuroinflammation (Mogensen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Neuroinflammation could further exacerbate waste product accumulation in interstitial and perivascular spaces. Eventually, this negative cycle can cause cognitive decline (Mogensen et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study indicated that the ALPS index, which reflects the impairment of ISF dynamics in the interstitial space, decreased and was related to cognitive function in the PSQ group. Thus, pathological backgrounds similar to those of AD patients have been reinforced in older adults with PSQ compared with those in HCs.\u003c/p\u003e \u003cp\u003eIn this study, CPV tended to increase in Japanese older adults with PSQ. Notably, the ALPS index was significantly negatively correlated with CPV in older adults with PSQ. The CP is a key regulator of CSF production and plays an important role in neurodegenerative, neuroinflammatory, and neuropsychological diseases (Municio et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The CP epithelium, which comprises the CSF\u0026ndash;blood\u0026ndash;brain barrier, has a great immunological brain interface. Alterations in the CSF\u0026ndash;blood\u0026ndash;brain barrier system with age could change the aspects of protective adaptive immunity in the central nervous system (Erickson and Banks, 2019). Several Aβ transporters, including LRP1, LRP2, RAGE, and AΒCB1, which are generally expressed at the blood\u0026ndash;brain barrier, have also been found in the CP epithelium on the CSF side (Crossgrove et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Fujiyoshi et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) reported that radiolabeled Aβ particles injected into lateral ventricles of rats were collected via the CP and subsequently eliminated from the CSF fivefold faster than if they were removed via CSF bulk flow alone. Therefore, CP may capture and remove proteins such as Aβ in the CSF, indicating that CP function is involved in clearance. CPV was negatively associated with CSF protein levels in healthy and diseased individuals, which could indicate that CP enlargement is linked to dysfunction in CSF production and/or clearance (Sayedhedayatollah et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tadayon et al., 2020). A higher CPV was correlated with a slower glymphatic clearance rate in all brain locations (Li et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and an enlarged CPV could be an indicator of greater growth of the WML, potentially involving impaired glymphatic function (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Furthermore, Tu et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) found a negative correlation between CPV and the ALPS index in patients with fibromyalgia (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.522, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). These findings suggest that CP enlargement is associated with glymphatic dysfunction. The findings in older adults with PSQ are consistent with those of previous reports. Therefore, the CP concentration tends to increase during glymphatic dysfunction caused by various conditions, including PSQ (Johnson et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study has several limitations. First, it was a single cohort study. However, large multicenter studies are needed to validate our results. Second, this study included only Japanese older adults. Therefore, this research should also be conducted in other races. Third, considering the use of the FreeSurfer technique, the CPV of the lateral ventricle, which is the main CP, was used in the analysis without the third or fourth ventricle. Fourth, there are only a few reports on CPV. In addition, the participants in this study were older adults, and the sample size was small. Thus, it was challenging to determine the factors for inclusion as covariates. In this study, sex, age, education year, SBP, DBP, MoCA score, ICV, and WMLV, which affect the indicators of the glymphatic system, including the ALPS index, were included as covariates in the analysis of CPV incidence. However, the factors that affect CPV in older adults must be further investigated. Finally, similar to the findings of previous studies, this study placed the ROIs at the level of the corona radiata and calculated the ALPS index, thereby allowing us to evaluate the water diffusion ability of perivascular spaces only in a part of the brain.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOlder adults with PSQ exhibited a decrease in the glymphatic system and an increase in CPV.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Juntendo University. All study participants or their legal guardians were briefed on the details of this study and provided written informed consent.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions\u003c/h2\u003e\n\u003cp\u003eJK conceived and designed the analysis, analyzed and interpreted the data, and drafted the manuscript. KK and KT performed the data acquisition and analyzed and interpreted the data. All the authors have read and revised this manuscript and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by JSPS KAKENHI (Grant Numbers 20K16737 and 21K15833); the MEXT-Supported Program for the Strategic Research Foundation at Private Universities; 2014\u0026ndash;2018 (S1411006) from the Ministry of Education, Culture, Sports, Science and Technology of Japan; and the Otsuka Toshimi Scholarship Foundation to SG.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors would like to thank Enago (www.enago.jp) for the English language review.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbott, N.J., Pizzo, M.E., Preston, J.E., Janigro, D., and Thorne, R.G. (2018). 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Choroid Plexus Enlargement and Allostatic Loading in Schizophrenia. \u003cem\u003eSchizophrenia Bulletin\u003c/em\u003e 46(3)\u003cstrong\u003e,\u003c/strong\u003e 722\u0026ndash;731. doi: 10.1093/schbul/sbz100.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Juntendo University Hospital","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":"poor sleep quality, glymphatic system, diffusion tensor imaging analysis along the perivascular space, diffusion-weighted image, cerebrospinal fluid, interstitial fluid, amyloid β, choroid plexus volume","lastPublishedDoi":"10.21203/rs.3.rs-4244404/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4244404/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to explore alterations in diffusion tensor image analysis along the perivascular space (DTI-ALPS) method and choroid plexus volume (CPV), which could be biomarkers of the glymphatic system in older adults with poor sleep quality (PSQ).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eFifty-two Japanese older adults with Pittsburgh Sleep Quality Index (PSQI) scores\u0026thinsp;\u0026gt;\u0026thinsp;5 (22 men and 30 women; mean age\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;73.10\u0026thinsp;\u0026plusmn;\u0026thinsp;5.67 years) and 52 healthy controls (HCs; PSQI score\u0026thinsp;\u0026le;\u0026thinsp;5) were included. Diffusion-weighted imaging (DWI) and 3D T1-weighted images were obtained using 3T magnetic resonance imaging. The ALPS index was calculated using preprocessed diffusion-weighted imaging (DWI), and the CPV was calculated using FreeSurfer 6.0. The mean ALPS index was subsequently compared between the PSQ group and HCs by using a general linear model (GLM) adjusted for covariates, including age, sex, years of education, intracranial volume, systolic blood pressure, diastolic blood pressure, hemoglobin A1c, and white matter lesion volume (WMLV). The CPV was also compared between the two groups by using the GLM, adjusting for the same covariates mentioned above. Next, we conducted a partial correlation analysis between the ALPS index and the CPV, Montreal Cognitive Assessment (MoCA), and PSQI scores, adjusting for all the abovementioned covariates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCompared with HCs, patients in the PSQ group had a significantly lower mean ALPS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.28) and a greater CPV (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11, Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24). In the PSQ group, the mean ALPS score was significantly negatively correlated with the CPV (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.35, false discovery rate [FDR]-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) but was significantly positively correlated with the MoCA score (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.35, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOlder adults with PSQ exhibited a decrease in the glymphatic system and an increase in CPV.\u003c/p\u003e","manuscriptTitle":"Glymphatic dysfunction and choroid plexus volume increase in older adults with poor sleep quality","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-04-11 15:27:28","doi":"10.21203/rs.3.rs-4244404/v2","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}},{"code":1,"date":"2024-04-11 06:38:14","doi":"10.21203/rs.3.rs-4244404/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":"e7dd12b9-4f13-473c-b108-157af73fb755","owner":[],"postedDate":"April 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30538061,"name":"Neurobiology of Disease"},{"id":30538062,"name":"Cellular \u0026 Molecular Neuroscience"}],"tags":[],"updatedAt":"2024-04-19T01:40:42+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-11 15:27:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-4244404","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4244404","identity":"rs-4244404","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}