Progression of cerebral small vessel disease in maintenance hemodialysis patients: a prospective cohort study

Progression of cerebral small vessel disease in maintenance hemodialysis patients: a prospective cohort 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 Research Article Progression of cerebral small vessel disease in maintenance hemodialysis patients: a prospective cohort study Zhou Xiaoling, Du Ye, Guo Yidan, Zhang Chunxia, Shi Zhihua, Jia Meng, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9536460/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Cerebral small vessel disease (CSVD) is a major vascular contributor to dementia, cognitive decline, gait impairment, mood disturbance and stroke. Maintenance hemodialysis (MHD) patients have a high CSVD prevalence with poor prognosis. However, the dynamic progressive features and associated risk factors of CSVD in MHD patients remain unclear. We aimed to explore the progression of CSVD subtypes and their associated risk factors in MHD patients. Method This prospective cohort study enrolled MHD patients from January to June 2023 at Beijing Shijitan Hospital. Clinical and dialysis-related data were collected. Transcranial Doppler (TCD) was used to monitor the mean flow velocity (MFV) of the middle cerebral artery during dialysis, and the MFV reduction rate during dialysis was calculated. The cerebral microbleeds (CMBs), white matter hyperintensities (WMHs), and Lacunes on brain magnetic resonance imaging (MRI) were assessed at baseline and at the 12-month follow-up. Multivariate linear regression model was used to analyze the risk factors associated with the progression of CSVD. Results A total of 92 MHD patients were included in this study, with a mean age of (63.21 ± 8.21) years (40–85 years old) and 71 males (77.17%). Compared with baseline, significant progression was observed in CMBs, WMHs and Lacunes at the 12-month follow-up ( p < 0.05). Multivariate linear regression analysis showed that longer dialysis vintage, higher MVF reduction rate, and elevated hsCRP levels were significantly associated with CMBs progression ( B = 0.065, 95% CI : 0.021–0.109, p = 0.004; B = 19.274, 95% CI : 0.379–38.168, p = 0.046; B = 0.311, 95% CI : 0.068–0.554, p = 0.013). Aging and increased MVF reduction rate were associated with WMHs progression ( B = 0.023, 95% CI : 0.006–0.040, p = 0.010; B = 1.352, 95% CI : 0.156–1.636, p = 0.013). In addition, smoking status and diabetes mellitus were significantly linked to lacunars progression ( B = 0.896, 95% CI : 0.156–1.636, p = 0.018; B = 1.230, 95% CI : 0.414–2.045, p = 0.004). Conclusion In addition to age, dialysis vintage, diabetes mellitus and smoking, the reduction in cerebral blood flow during hemodialysis is an independent risk factor for the progression of CSVD in MHD patients. Renal dialysis Cerebral small vessel disease Cerebrovascular circulation Cerebral blood flow Figures Figure 1 Figure 2 Introduction Cerebral small vessel disease (CSVD) is a heterogeneous group of diseases caused by in situ damage of small brain vessels[ 1 – 4 ]. Cardinal neuroimaging features include cerebral microbleeds (CMBs), white matter hyperintensities (WMHs), and lacunar infarcts (Lacunes)[ 5 , 6 ]. CSVD represents one of the major problems facing global society today, causing a quarter of all ischemic strokes and the vast majority of spontaneous hemorrhages and accounting for 20% or more of all dementias[ 1 , 7 – 9 ]. Patients undergoing maintenance hemodialysis (MHD) are at high risk of CSVD due to special pathological conditions. Previous studies have shown an elevated prevalence of CMBs (19.3 ~ 35%), WMHs (52 ~ 76.7%) and Lacunes (35.7 ~ 55.5%,) in MHD patients, with an earlier age of onset than the general population[ 10 , 11 ]. Notably, CSVD in MHD patients is significantly associated with an increased risk of stroke, cardiovascular death, and cognitive impairment, which severely compromises the long-term prognosis and quality of life[ 12 – 14 ]. Despite the high prevalence and poor prognosis of CSVD in MHD patients, its underlying mechanism is still not fully explained[ 15 , 16 ]. Most previous studies have been cross-sectional, focusing on the prevalence and baseline characteristics of CSVD among MHD patients, which makes it difficult to reveal the dynamic progressive features and associated risk factors of CSVD subtypes, including CMBs, WMHs, and Lacunes. Furthermore, cerebral blood flow (CBF) changes commonly occur during hemodialysis[ 12 , 17 ]. However, whether such dialysis-related factors are associated with CSVD progression in MHD patients remains unclear, which limits the targeted preventive and therapeutic strategies. Therefore, we present a prospective investigation of MHD patients over a 12-month follow-up. The primary aim were: (1) investigate the progression of CSVD (including CMBs, WMHs, and Lacunes) in MHD patients; (2) identify the risk factors associated with the progression of different CSVD subtypes, with a particular focus on the relationship between the intradialytic CBF alterations and CSVD progression. Methods Study design We conducted a prospective observational study focusing on patients undergoing maintenance hemodialysis, collecting clinical and dialysis-related data. Brain magnetic resonance imaging (MRI) examinations were performed at baseline and 12-month follow-up, and we analyzed the progression in CSVD and its associated risk factors. Study participants Eligible patients were recruited from the hemodialysis centers of Beijing Shijitan Hospital, affiliated to Capital Medical University from January to June 2023. The inclusion criteria were as follows: (1) End-Stage Renal Disease with maintenance hemodialysis treatment for at least 3 months, (2) age ≥ 18 years, (3) willing to join the study and provide written informed consent. The exclusion criteria were as follows: (1) unable to cooperate with transcranial Doppler (TCD) and brain MRI examinations, (2) experienced disturbance of consciousness or recently diagnosed with psychosis, (3) had severe comorbidities with an expected survival of less than 1 year, (4) had a plan for kidney transplantation within 12 months of the baseline assessment. This study was conducted in accordance with the Declaration of Helsinki. The ethical approval for this study was granted by the Institutional Ethical Review Board of Beijing Shijitan Hospital, Capital Medical University [sjtkyll-lx-2022-078]. Written informed consent was obtained from each participant. Clinical variables Demographic data and basic characteristics including gender, age, smoking, alcohol intake, body mass index (BMI), primary kidney disease, history of hypertension, diabetes mellitus, stroke, cardiovascular disease (CVD), and medication use were recorded at the time of enrollment. Blood tests, including hemoglobin, serum albumin, lipid profiles, calcium, phosphate, intact parathyroid hormone (iPTH), and high-sensitivity C-reactive protein (hsCRP), were performed before the first hemodialysis session in a week. Hemodialysis-related variables Dialysis-related data including dialysis vintage, vascular access, anticoagulation mode, urea reduction index (Kt/V), ultrafiltration rate (UFR) were recorded at enrollment. Systolic blood pressure (SBP), diastolic blood pressure (DBP) and the mean flow velocity (MFV) of the middle cerebral artery (MCA) were measured 15 minutes pre-dialysis and immediately post-dialysis, respectively. Mean arterial pressure (MAP) was calculated as DBP་(SBP-DBP)/3. MFV was monitored by TCD ultrasound. ΔMFV = MFV (15 minutes pre-dialysis)།MFV (post-dialysis), MFV reduction rate = ΔMFV/MFV (15 minutes pre-dialysis). To minimize measurement errors, all Doppler readings were obtained by two trained operators in strict accordance with standardized procedures. Imaging assessment of CSVD We assessed CVSD in MHD patients using brain MRI at two time points: baseline and 12 months follow-up, and further described the progression of CVSD. Brain MRI scans were performed using a 3.0 T magnetic resonance imaging system (Philips, the Netherlands) by experienced physicians who had received standardized training. The following imaging sequences were acquired: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and susceptibility-weighted imaging (SWI). CMBs were defined as small areas of signal void (usually 2 ~ 5 mm or sometimes 10 mm in size) with associated blooming artifact on SWI sequences. Anatomic localization of CMBs includes lobe (frontal, parietal, temporal, occipital, and insular lobes), deep (basal ganglia, thalamus, internal capsule, external capsule, corpus callosum, and white matter), and infratentorial (brain stem and cerebellum)[ 5 ]. The number of CMB lesions was recorded. WMHs typically present as hyperintensities of variable size in the white matter on T2WI and FLAIR sequences, without cavitation (signal different from cerebrospinal fluid), and typically symmetrical between hemispheres[ 5 ]. Deep WMHs (DWMHs) and periventricular WMHs (PVWMHs) were graded separately from 0 to 3 according to the Fazekas scale, with the total Fazekas score calculated as the sum of these 2 parts (0 ~ 6)[ 18 ]. Lacunes were defined as focal lesions of 3 ~ 15 mm in size, with the same signal characteristics as cerebrospinal fluid on all MRI sequences[ 5 ]. The number of lacunar lesions was recorded. Progression in CSVD at the 12 months follow-up were evaluated using the following calculation formulas: ΔCMBs = Number of CMB lesions (12 month-baseline); ΔWMHs = Fazekas score (12 months།baseline); ΔLacunes = Number of lacunar lesions (12 months།baseline). Statistical analysis Baseline characteristics were presented in descriptive statistics, with mean and standard deviation (SD) or median with interquartile range (IQR) given for continuous variables, and percentages given for categorical variables. CSVD at baseline and 12 months were compared using the paired samples t -test or Wilcoxon signed-rank test. Multivariable linear regression model was used to identify the risk factors for CSVD progression, with ΔCMBs, ΔWMHs, and ΔLacunes designated as the dependent variables, respectively. Variables with p < 0.10 in the univariate analysis, as well as potential clinical risk factors for CSVD reported previously were incorporated into the multivariable linear regression model. A two-tailed p value < 0.05 was considered statistically significant. All analyses were performed with SPSS version 29.0 statistical software (SPSS Inc., Chicago, IL, USA). Results Baseline characteristics of participants A total of 113 patients were initially enrolled in this study. During the follow-up period, 10 patients expired and 11 patients withdrew for various reasons. Finally, 92 patients were finished the follow-up (Fig.1). Baseline characteristics of the remaining 92 patients are shown in Table 1, the mean age was (63.21±8.21) years, 77.17% were men. Hypertension (91.30%), diabetes mellitus (66.30%) and CVD (61.96%) were the most common comorbidities. The median dialysis vintage was 33 (16, 113) months at baseline and mean weekly hemodialysis duration was (11.40 ± 0.78) h. 57 patients received anticoagulation with heparin, 30 with low-molecular-weight heparin, and 5 without any anticoagulants. The dialysis blood flow rate was 220~300 mL/min, the dialysate flow was 500 mL/min, and dialysate temperature was 36.5°C. The mean MAP was (104.78±13.26) mmHg pre-hemodialysis and (112.52±13.34) mmHg post-hemodialysis. MFV decreased from (53.28±13.99) m/s pre- hemodialysis to (47.13±14.09) m/s at the end of hemodialysis, and the mean MFV reduction rate was (11.43±15.19) %. Hemodialysis-related clinical data are reported in Table 2. Table 1 Demographic and clinical characteristics of participants at baseline Variables Value N 92 Man, n (%) 71 (77.17) Age (year) 63.21 ± 8.21 Primary kidney disease, n (%) Glomerulonephritis 26 (28.26) Diabetes 43 (46.74) Vascular 14 (15.22) Other diagnosis 9 (9.78) Comorbidities, n (%) Hypertension 84 (91.30) Diabetes mellitus 61 (66.30) Stroke 18 (19.57) CVD 57 (61.96) Medication, n (%) CCB 61 (66.30) ACEI /ARB 34 (36.96) B-blocker 28 (30.43) Nitrate 18 (19.57) Antiplatelet 29 (31.52) Smoking status, n (%) 39 (42.39) Alcohol intake, n (%) 14 (15.22) BMI (kg/m 2 ) 23.58（20.35, 25.68） Laboratory examinations HGB (g/L) 113.35 ± 13.54 ALB (g/L) 36.54 ± 3.15 TC (mmol/L) 3.41±0.78 TG ( mmol/L) 1.47 ± 0.91 HDL-C (mmol/L) 1.05±0.24 LDL-C (mmol/L) 1.95±0.65 hsCRP (mg/L) 4.43 (1.60, 8.04) Calcium (mmol/L) 2.16±0.16 Phosphate (mmol/L) 1.78±0.55 iPTH (pg/mL) 160.90 (98.20, 278.00) Note: Values are presented as mean ± standard deviation for continuous variables with normal distribution, and median (interquartile range) for continuous variables with skewed distribution; n (%) for categorical variables Abbreviations: CVD, cardiovascular disease. CCB, calcium channel blockers. ACEI, angiotensin converting enzyme inhibitors. ARB, angiotensin Ⅱ receptor blockers. BMI, body mass index. HGB, hemoglobin. ALB, albumin. TC, total cholesterol. TG, triglycerides. HDL-C, high-density lipoprotein cholesterol. LDL-C, low-density lipoprotein cholesterol. hsCRP, high-sensitivity C-reactive protein. iPTH, intact parathyroid hormone Table 2 Hemodialysis-related clinical data of participants at baseline Variables Value N 92 Dialysis vintage (months) 33 (16, 113) Dialysis treatment time (h/week) 11.41±0.78 Kt/V 1.19 ± 0.22 Vascular access, n (%) CVC 71 (77.17) AVF 19 (20.65) AVG 2 (2.17) Anticoagulation mode, n (%) UFH 57 (61.96) LMWH 30 (32.61) No anticoagulation 5 (5.43) Pre-hemodialysis SBP (mmHg) 156.75±20.30 DBP (mmHg) 78.79±12.51 MAP (mmHg) 104.78 ± 13.26 MFV (cm/s) 53.28±13.99 Post-hemodialysis SBP (mmHg) 168.39±17.10 DBP (mmHg) 84.59±14.04 MAP (mmHg) 112.52±13.34 MFV (cm/s) 47.13±14.09 MVF reduction rate (%) 11.43 ± 15.19 UFR (mL/h) 428.86 ± 233.60 Note: Values are presented as mean ± standard deviation for continuous variables with normal distribution, and median (interquartile range) for continuous variables with skewed distribution; n (%) for categorical variables Abbreviations: Kt/V, urea reduction index. CVC, central venous catheter. AVF, autologous arteriovenous fistula. AVG, arteriovenous graft. UFH, unfractionated heparin. LMWH, low molecular weight heparin. SBP, systolic blood pressure. DBP, diastolic blood pressure. MAP, mean arterial pressure. MVF, mean flow velocity. UFR, ultrafiltration rate CSVD at baseline and 12 months follow-up At baseline, among the 92 MHD patients, 56 (60.87%) had CSVD, including 33 (35.87%) with CMBs, 38 (41.30%) with WMHs and 30 (32.61%) with Lacunes. After 12 months of follow-up, 63 (68.48%) had CSVD, including 38 (41.30 %) with CMBs, 40 (43.48%) with WMHs and 33 (35.87%) with Lacunes. Compared with the brain MRI data at baseline, the counts of CMBs and lacunar, and Fazekas scores of WMHs all significantly increased at 12 months (all p values < 0.05). The results are shown in Table 3, and representative MRI images of CSVD are illustrated in Fig.2. Table 3 Comparison of CSVD imaging markers at baseline and 12-month follow-up Index Baseline 12 Months t/Z v alue P -v alue CBMs (count) Total Count 17.00 (5.00, 36.50) 23.00 (12.00, 44.00) -7.873 <0.001 Lobe 4.50 (1.00, 10.75) 7.50 (2.00, 14.00) -6.267 <0.001 Deep 4.00 (2.00, 14.00) 7.50 (2.00, 18.00) -5.221 <0.001 Infratentorial 5.00 (1.00, 9.00) 8.00 (2.00, 12.00) -6.933 <0.001 WMHs (Fazekas Score) Total Score 3.623±1.30 3.88±1.35 -4.532 <0.001 Periventricular White Matter 2.03±0.86 2.22±0.81 -4.001 <0.001 Deep White Matter 1.53±0.70 1.68±0.85 -2.392 0.019 Lacunes (Count) 1.00 (0.00, 2.00) 2.50 (1.00, 4.00) -6.746 <0.001 Note: Values are presented as mean ± standard deviation for continuous variables with normal distribution, and median (interquartile range) for continuous variables with skewed distribution Abbreviations: CMBs, cerebral microbleeds. WMHs, white matter hyperintensities Associated f actors of CSVD progression in MHD patients Multiple linear regression analysis showed several significant associations with CSVD progression (Table 4). Longer dialysis vintage, higher MVF reduction rate, and elevated hsCRP levels were significantly associated with ΔCMBs ( B = 0.065, 95% CI : 0.021–0.109, p = 0.004; B = 19.274, 95% CI : 0.379-38.168, p = 0.046; B = 0.311, 95% CI : 0.068–0.554, p = 0.013). Aging and increased MVF reduction rate were associated with ΔWMHs ( B = 0.023, 95% CI : 0.006–0.040, p = 0.010; B = 1.352, 95% CI : 0.156-1.636, p = 0.013). In addition, smoking status and diabetes mellitus were significant risk factors for ΔLacunes ( B =0.896, 95% CI : 0.156-1.636, p = 0.018; B = 1.230, 95% CI : 0.414-2.045, p = 0.004). Table 4 Multivariable linear regression analysis of risk factors for CSVD progression Variables B 95% CI for B P -value ΔCMBs Dialysis vintage 0.065 0.021-0.109 0.004 MVF reduction rate 19.274 0.379-38.168 0.046 hsCRP 0.311 0.068-0.554 0.013 ΔWMHs Age 0.023 0.006-0.040 0.010 MVF reduction rate 1.352 0.190-2.514 0.023 ΔLacunes Smoking status 0.896 0.156-1.636 0.018 Diabetes mellitus 1.230 0.414-2.045 0.004 In multivariable linear regression analysis for ΔWMHs and ΔLacunes, age, gender, hypertension, diabetes mellitus, smoking, dialysis vintage, MVF reduction rate, UFR, HGB, ALB, and hsCRP were adjusted; for ΔCMBs, anticoagulation mode, TG were additionally adjusted on the basis of the aforementioned covariates Abbreviations: CMBs, cerebral microbleeds. WMHs, white matter hyperintensities. MVF, mean flow velocity. UFR, ultrafiltration rate. HGB, hemoglobin. ALB, albumin. hsCRP, high-sensitivity C-reactive protein. TG, triglycerides Discussion CSVD is highly prevalent among MHD patients and accompanied by poor clinical prognosis[11]. However, the etiology is complex and underlying mechanisms remain unclear[2]. Furthermore, longitudinal studies exploring CSVD progression in this cohort remain scarce and are limited by small sample sizes. In this 12-month prospective follow-up study of MHD patients, we observed significant progression in the numbers of CMBs and Lacunes, as well as the Fazekas score for WMHs, compared with baseline. Further multivariate linear regression analysis shown different risk factors for the progression of CSVD subtypes. Longer dialysis vintage, higher MFV reduction rate, and elevated hsCRP levels were significantly associated with CMBs progression. Aging and increased MFV reduction rate were independent risk factors for WMHs progression; while smoking and diabetes mellitus were significantly associated with Lacunes progression. These findings help further explain the potential mechanisms underlying CSVD in MHD patients, and allow for more careful monitoring and targeted intervention measures. WMHs prevalence increases with age, it can be found in 20% of adults in their sixties and in up to 94% in the population of octogenarians[19]. In present study, aging remained an independent risk factor for WMHs progression among MHD patients, which is consistent with findings in the general population[10]. Lacunes result from the occlusion of small perforating arteries. Diabetes mellitus and smoking have been established as important risk factors[20]. Diabetes accelerates atherosclerosis and thrombosis in small penetrating arteries by promoting the formation of advanced glycation end products and increasing blood viscosity[21-23]. Smoking, in turn, impairs vascular endothelial function through vasospasm and oxidative stress[23, 24]. Collectively, these processes raise the risk of occlusion in deep cerebral small arteries in patients undergoing maintenance hemodialysis, thereby facilitating the development of Lacunes[23]. These findings emphasize that tight glycemic control and smoking cessation represent key interventions for preventing Lacunes in this patient population. hsCRP is a sensitive marker of chronic inflammation[25]. The present study showed that elevated hsCRP levels are an independent risk factor for CMBs progression in MHD patients. MHD patients often present with persistent systemic low‑grade inflammation due to multiple factors, such as uremic toxin stimulation, poor biocompatibility of dialysis membranes, and repeated infections etc[26-28]. The chronic inflammation induces a state of endothelial cell dysfunction impairing cerebral blood flow regulation and breaking the blood brain barrier[29]. Combined with hemodynamic fluctuations during dialysis, it results in neuronal injury and CSVD clinical manifestations. Notably, the intradialytic MFV reduction rate is a risk factor for the progression of both CMBs and WMHs. Previous studies have demonstrated evidence of decreased CBF in MHD patients during hemodialysis treatment, but whether such short-term CBF changes are associated with CSVD progression remains unclear. Kalantari et al.[30] used pseudo continuous arterial spin labeling magnetic resonance imaging (pCASL-MRI) to measure CBF in CSVD patients, and the results showed a significant negative correlation between CBF and the Fazekas score of WMHs. However, MHD patients were not included in the study cohort, and thus could not define the specific role of dialysis-related CBF changes. Findlay et al.[12]reported that patients undergoing hemodialysis experience transient decline in CBF. The percentage of decline in MFV correlated significantly with progression of WMHs burden in patients on continued dialysis, but not in transplanted patients. Li et al.[31]found that CBF in hemodialysis patients could manifest as both increased and decreased, compared with the increased CBF group, WMH in patients with decreased CBF developed severely with prolongation of hemodialysis duration. These findings support the impact of reduced CBF on WMHs in MHD patients. However, they did not analyze the association between the rate of CBF decline and the progression of WMHs, nor did they explore other subtypes of CSVD such as CMBs. The present study analyzed the association between the rate of MFV reduction and multiple CSVD subtypes. After adjusting for confounding factors, the intradialytic MFV reduction rate is an independent risk factor for the progression of both WMHs and CMBs. CBF reduction during hemodialysis may further exacerbate the hypoxic state of brain tissue, and cerebral hypoperfusion can induce endothelial cell injury and blood-brain barrier dysfunction, thereby promoting the progression of CMBs and WMHs[32]. This finding indicates that maintaining stable cerebral hemodynamics during hemodialysis may be crucial for preventing CSVD progression. Limitations The present prospective study identified risk factors associated with CSVD progression in patients undergoing maintenance hemodialysis. However, it still has several limitations. First, it is a single-center study, which may affect the generalizability and extrapolation of the results. Second, some patients were lost to follow-up, which may introduce selection bias. Due to these limitations, further verification through multi-center studies with large sample sizes is required. Conclusions In conclusion, CSVD progression in MHD patients is driven by the combined effects of traditional vascular risk factors and dialysis-related factors. Clinically, individualized strategies should be developed, with enhanced monitoring and intervention of high-risk factors to reduce the risk of adverse outcomes. Abbreviations CSVD Cerebral small vessel disease MHD Maintenance hemodialysis TCD Transcranial Doppler MFV Mean flow velocity MRI Magnetic resonance imaging CMBs Cerebral microbleeds WMHs White matter hyperintensities CBF Cerebral blood flow BMI Body mass index CVD Cardiovascular disease UFR Ultrafiltration rate SBP Systolic blood pressure DBP Diastolic blood pressure MCA Middle cerebral artery MAP Mean arterial pressure DWMHs Deep white matter hyperintensities PVWMHs Periventricular white matter hyperintensities CVC Central venous catheter AVF Autologous arteriovenous fistula AVG Arteriovenous graft UFH Unfractionated heparin LMWH Low molecular weight heparin IQR Interquartile range HGB Hemoglobin ALB Albumin TG Triglycerides iPTH Intact parathyroid hormone hsCRP High-sensitivity C-reactive protein Declarations Author contributions XLZ: Data collection, analysis, manuscript writing. YDG: Statistical data analysis. CXZ, ZHS, MJ, JYS, XYZ, XYS: Data collection. YL, YD, YDG: Study design, manuscript revision. Funding Capital’s Funds for Health Improvement and Research (CFH 2022-2-2081); Beijing Municipal Science & Technology Commission national key research and development plan matching project (Z161100002616005). Data availability The datasets analyzed in the current study are not publicly available to ensure the privacy of research participants and comply with the regulations of the ethics approval. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics approval and consent to participate The study has been approved by the Ethics Committee of Beijing Shijitan Hospital, Capital Medical University [Ethics Approval Number: sjtkyll-lx-2022-078]. Informed consent was obtained from all study participants. The study was conducted following the Declaration of Helsinki. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Markus HS, Joutel A. The pathogenesis of cerebral small vessel disease and vascular cognitive impairment. Physiol Rev. 2025;105(3):1075-171. Dupré N, Drieu A, Joutel A. Pathophysiology of cerebral small vessel disease: a journey through recent discoveries. J Clin Invest. 2024;134(10):e172841. Chojdak-Łukasiewicz J, Dziadkowiak E, Zimny A,et al. Cerebral small vessel disease: A review. Adv Clin Exp Med. 2021;30(3):349-56. Hainsworth AH, Markus HS, Schneider JA. Cerebral Small Vessel Disease, Hypertension, and Vascular Contributions to Cognitive Impairment and Dementia. Hypertension. 2024;81(1):75-86. Duering M, Biessels GJ, Brodtmann A, et al. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol. 2023;22(7):602-18. Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822-38. Cannistraro RJ, Badi M, Eidelman BH, et al. CNS small vessel disease: A clinical review. Neurology. 2019;92(24):1146-56. Lamar M, Leurgans S, Kapasi A, et al. Complex Profiles of Cerebrovascular Disease Pathologies in the Aging Brain and Their Relationship With Cognitive Decline. Stroke. 2022;53(1):218-27. Hairus S MN, Srinivasan A, Shaik Mohideen SA, et al. Cerebral Small-Vessel Disease Markers on Magnetic Resonance Imaging as Predictors of Recurrent Vascular Events and Death in Ischemic Stroke: A Systematic Review and Meta-Analysis. J Am Heart Assoc. 2025;14(17):e040082. Naganuma T, Takemoto Y. Asymptomatic Cerebrovascular Disease in Dialysis Patients. Contrib Nephrol. 2018;196:22-6. Zheng K, Zhou Y, Qian Y, et al. Increased Premature Cerebral Small Vessel Diseases in Dialysis Patients: A Retrospective Cross-Sectional Study. Nephron. 2021;145(4):330-41. Findlay MD, Dawson J, Dickie DA,et al. Investigating the Relationship between Cerebral Blood Flow and Cognitive Function in Hemodialysis Patients. J Am Soc Nephrol. 2019;30(1):147-58. Qian Y, Zheng K, Wang H, et al. Cerebral microbleeds and their influence on cognitive impairment in Dialysis patients. Brain Imaging Behav. 2021;15(1):85-95. Angermann S, Günthner R, Hanssen H, et al. Cognitive impairment and microvascular function in end-stage renal disease. Int J Methods Psychiatr Res. 2022;31(2):e1909. Bi R, Wei Y, Li P, et al. Associations of Cerebral Small Vessel Disease and Chronic Kidney Disease in Patients With Acute Ischemic Stroke. J Am Heart Assoc. 2025;14(9):e038711. Lau WL, Huisa BN, Fisher M. The Cerebrovascular-Chronic Kidney Disease Connection: Perspectives and Mechanisms. Transl Stroke Res. 2017;8(1):67-76. Shi L, Zheng K, Qian Y, et al. Effect of ultrafiltration on cerebral small-vessel disease and related outcomes in hemodialysis. Clin Kidney J. 2023;16(7):1139-48. Fazekas F, Chawluk JB, Alavi A, et al. MR signal abnormalities at 1.5 T in Alzheimer's dementia and normal aging. AJR Am J Roentgenol. 1987;149(2):351-6. Garde E, Mortensen EL, Krabbe K, et al. Relation between age-related decline in intelligence and cerebral white-matter hyperintensities in healthy octogenarians: a longitudinal study. Lancet. 2000;356(9230):628-34. Litak J, Mazurek M, Kulesza B, et al. Cerebral Small Vessel Disease. Int J Mol Sci. 2020;21(24):9729. Paneni F, Beckman JA, Creager MA,et al. Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part I. Eur Heart J. 2013;34(31):2436-43. Twarda-Clapa A, Olczak A, Białkowska AM, et al. Advanced Glycation End-Products (AGEs): Formation, Chemistry, Classification, Receptors, and Diseases Related to AGEs. Cells. 2022;11(8):1312. Wang Z, Chen Q, Chen J, et al. Risk factors of cerebral small vessel disease: A systematic review and meta-analysis. Medicine (Baltimore). 2021;100(51):e28229. Fetterman JL, Keith RJ, Palmisano JN, et al. Alterations in Vascular Function Associated With the Use of Combustible and Electronic Cigarettes. J Am Heart Assoc. 2020;9(9):e014570. Queissner R, Fellendorf FT, Dalkner N, et al. The influence of chronic inflammation on the illnesscourse of bipolar disorder: A longitudinal study. J Psychiatr Res. 2024;174:258-62. Cobo G, Lindholm B, Stenvinkel P. Chronic inflammation in end-stage renal disease and dialysis. Nephrol Dial Transplant. 2018;33(suppl_3):iii35-35iii40. Mukai H, Villafuerte H, Qureshi AR, et al. Serum albumin, inflammation, and nutrition in end-stage renal disease: C-reactive protein is needed for optimal assessment. Semin Dial. 2018;31(5):435-9. Silva RE, Santos EC, Justino P, et al. Cytokines and chemokines systemic levels are related to dialysis adequacy and creatinine clearance in patients with end-stage renal disease undergoing hemodialysis. Int Immunopharmacol. 2021;100:108154. Hannawi Y. Cerebral Small Vessel Disease: a Review of the Pathophysiological Mechanisms. Transl Stroke Res. 2024;15(6):1050-69. Kalantari S, Soltani M, Maghbooli M, et al. Cerebral blood flow alterations measured by ASL-MRI as a predictor of vascular dementia in small vessel ischemic disease. Radiologia (Engl Ed). 2025;67(1):28-37. Li M, Yang W, Song L, et al. Association between white matter hyperintensities and altered cerebral blood flow in maintenance hemodialysis patients: a longitudinal study. BMC Nephrol. 2024;25(1):33. McIntyre CW. Update on Hemodialysis-Induced Multiorgan Ischemia: Brains and Beyond. J Am Soc Nephrol. 2024;35(5):653-64. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 04 May, 2026 Editor invited by journal 04 May, 2026 Editor assigned by journal 30 Apr, 2026 Submission checks completed at journal 30 Apr, 2026 First submitted to journal 27 Apr, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9536460","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637385719,"identity":"db928fad-bb65-475f-8a12-ce58f087fb74","order_by":0,"name":"Zhou Xiaoling","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhou","middleName":"","lastName":"Xiaoling","suffix":""},{"id":637385720,"identity":"e011082f-8ff9-47f6-bdbe-c0bbcf4862f3","order_by":1,"name":"Du Ye","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Du","middleName":"","lastName":"Ye","suffix":""},{"id":637385721,"identity":"4ef121ad-4be2-4365-bbd7-cae798072bcf","order_by":2,"name":"Guo Yidan","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guo","middleName":"","lastName":"Yidan","suffix":""},{"id":637385722,"identity":"96714f41-9a7d-4838-b167-c86ee6235684","order_by":3,"name":"Zhang Chunxia","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Chunxia","suffix":""},{"id":637385723,"identity":"c69a9757-f907-4fb4-8e77-8de72188d7f5","order_by":4,"name":"Shi Zhihua","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shi","middleName":"","lastName":"Zhihua","suffix":""},{"id":637385724,"identity":"b401ef44-7bd3-4228-bce1-2f6bf8544508","order_by":5,"name":"Jia Meng","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Meng","suffix":""},{"id":637385725,"identity":"8e505a40-6b62-40b0-911b-f5d9970cafee","order_by":6,"name":"Sun Jingying","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sun","middleName":"","lastName":"Jingying","suffix":""},{"id":637385726,"identity":"0adba881-282c-43a0-8ed1-80fa02148766","order_by":7,"name":"Zhang Xiyou","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Xiyou","suffix":""},{"id":637385727,"identity":"2edd632a-02c5-4ec6-9e81-4f0e5de21614","order_by":8,"name":"Song Xinyu","email":"","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Xinyu","suffix":""},{"id":637385728,"identity":"8dd6999f-e3b3-4718-a4d5-378d2336e6ad","order_by":9,"name":"Luo Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYBACPhDxsYGNgbGBh0gtbEDMOBOkpY0ULcy8DSAW0Vokko9J2+7gs2ee33uA4ecOorSkpUnnnmFLbGzjS2DsPUOUlhwz6dw2tgSgXwyYGduI1WLZxmZPohbGNjbGRuK18DxLtuwF+yXH4GAvMVr42ZMP3vi545i9YfMZwwc/idECBCwSDAzHGAwbGBgOEKcBGJMfGBhqGOSJVT4KRsEoGAUjDwAABfotk5NqrTsAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Shijitan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Luo","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2026-04-27 05:08:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9536460/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9536460/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109119250,"identity":"25dd7d11-070d-4d53-a321-3e626f885d9f","added_by":"auto","created_at":"2026-05-12 16:56:54","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111747,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of patient enrollment\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9536460/v1/581b74c128549488194a97dd.jpg"},{"id":109119055,"identity":"029466fb-4d70-4f7e-a4e5-50d1475c96a4","added_by":"auto","created_at":"2026-05-12 16:56:21","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":129487,"visible":true,"origin":"","legend":"\u003cp\u003eCranial MRI findings of CSVD in MHD patients\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e Cerebral microbleeds; \u003cstrong\u003eB\u003c/strong\u003e White matter hyperintensities; \u003cstrong\u003eC \u003c/strong\u003e\u0026nbsp;Lacunes\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9536460/v1/a683ccfbaa4fd6ad48e3944f.jpg"},{"id":109119306,"identity":"a56c2c79-ab52-4e19-84dd-493e5583b3ab","added_by":"auto","created_at":"2026-05-12 16:57:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":650195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9536460/v1/0d95a42b-5752-4023-a9af-eb19153bb947.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Progression of cerebral small vessel disease in maintenance hemodialysis patients: a prospective cohort study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCerebral small vessel disease (CSVD) is a heterogeneous group of diseases caused by in situ damage of small brain vessels[\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cardinal neuroimaging features include cerebral microbleeds (CMBs), white matter hyperintensities (WMHs), and lacunar infarcts (Lacunes)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CSVD represents one of the major problems facing global society today, causing a quarter of all ischemic strokes and the vast majority of spontaneous hemorrhages and accounting for 20% or more of all dementias[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients undergoing maintenance hemodialysis (MHD) are at high risk of CSVD due to special pathological conditions. Previous studies have shown an elevated prevalence of CMBs (19.3\u0026thinsp;~\u0026thinsp;35%), WMHs (52\u0026thinsp;~\u0026thinsp;76.7%) and Lacunes (35.7\u0026thinsp;~\u0026thinsp;55.5%,) in MHD patients, with an earlier age of onset than the general population[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Notably, CSVD in MHD patients is significantly associated with an increased risk of stroke, cardiovascular death, and cognitive impairment, which severely compromises the long-term prognosis and quality of life[\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the high prevalence and poor prognosis of CSVD in MHD patients, its underlying mechanism is still not fully explained[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Most previous studies have been cross-sectional, focusing on the prevalence and baseline characteristics of CSVD among MHD patients, which makes it difficult to reveal the dynamic progressive features and associated risk factors of CSVD subtypes, including CMBs, WMHs, and Lacunes. Furthermore, cerebral blood flow (CBF) changes commonly occur during hemodialysis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, whether such dialysis-related factors are associated with CSVD progression in MHD patients remains unclear, which limits the targeted preventive and therapeutic strategies.\u003c/p\u003e \u003cp\u003eTherefore, we present a prospective investigation of MHD patients over a 12-month follow-up. The primary aim were: (1) investigate the progression of CSVD (including CMBs, WMHs, and Lacunes) in MHD patients; (2) identify the risk factors associated with the progression of different CSVD subtypes, with a particular focus on the relationship between the intradialytic CBF alterations and CSVD progression.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe conducted a prospective observational study focusing on patients undergoing maintenance hemodialysis, collecting clinical and dialysis-related data. Brain magnetic resonance imaging (MRI) examinations were performed at baseline and 12-month follow-up, and we analyzed the progression in CSVD and its associated risk factors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy participants\u003c/h3\u003e\n\u003cp\u003eEligible patients were recruited from the hemodialysis centers of Beijing Shijitan Hospital, affiliated to Capital Medical University from January to June 2023. The inclusion criteria were as follows: (1) End-Stage Renal Disease with maintenance hemodialysis treatment for at least 3 months, (2) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, (3) willing to join the study and provide written informed consent. The exclusion criteria were as follows: (1) unable to cooperate with transcranial Doppler (TCD) and brain MRI examinations, (2) experienced disturbance of consciousness or recently diagnosed with psychosis, (3) had severe comorbidities with an expected survival of less than 1 year, (4) had a plan for kidney transplantation within 12 months of the baseline assessment.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki. The ethical approval for this study was granted by the Institutional Ethical Review Board of Beijing Shijitan Hospital, Capital Medical University [sjtkyll-lx-2022-078]. Written informed consent was obtained from each participant.\u003c/p\u003e\n\u003ch3\u003eClinical variables\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDemographic data and basic characteristics including gender, age, smoking, alcohol intake, body mass index (BMI), primary kidney disease, history of hypertension, diabetes mellitus, stroke, cardiovascular disease (CVD), and medication use were recorded at the time of enrollment. Blood tests, including hemoglobin, serum albumin, lipid profiles, calcium, phosphate, intact parathyroid hormone (iPTH), and high-sensitivity C-reactive protein (hsCRP), were performed before the first hemodialysis session in a week.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eHemodialysis-related variables\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDialysis-related data including dialysis vintage, vascular access, anticoagulation mode, urea reduction index (Kt/V), ultrafiltration rate (UFR) were recorded at enrollment. Systolic blood pressure (SBP), diastolic blood pressure (DBP) and the mean flow velocity (MFV) of the middle cerebral artery (MCA) were measured 15 minutes pre-dialysis and immediately post-dialysis, respectively. Mean arterial pressure (MAP) was calculated as DBP་(SBP-DBP)/3. MFV was monitored by TCD ultrasound. ΔMFV\u0026thinsp;=\u0026thinsp;MFV (15 minutes pre-dialysis)།MFV (post-dialysis), MFV reduction rate\u0026thinsp;=\u0026thinsp;ΔMFV/MFV (15 minutes pre-dialysis). To minimize measurement errors, all Doppler readings were obtained by two trained operators in strict accordance with standardized procedures.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eImaging assessment of CSVD\u003c/h3\u003e\n\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWe assessed CVSD in MHD patients using brain MRI at two time points: baseline and 12 months follow-up, and further described the progression of CVSD.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eBrain MRI scans were performed using a 3.0 T magnetic resonance imaging system (Philips, the Netherlands) by experienced physicians who had received standardized training. The following imaging sequences were acquired: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), diffusion-weighted imaging (DWI), and susceptibility-weighted imaging (SWI).\u003c/p\u003e \u003cp\u003eCMBs were defined as small areas of signal void (usually 2\u0026thinsp;~\u0026thinsp;5 mm or sometimes 10 mm in size) with associated blooming artifact on SWI sequences. Anatomic localization of CMBs includes lobe (frontal, parietal, temporal, occipital, and insular lobes), deep (basal ganglia, thalamus, internal capsule, external capsule, corpus callosum, and white matter), and infratentorial (brain stem and cerebellum)[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The number of CMB lesions was recorded.\u003c/p\u003e \u003cp\u003eWMHs typically present as hyperintensities of variable size in the white matter on T2WI and FLAIR sequences, without cavitation (signal different from cerebrospinal fluid), and typically symmetrical between hemispheres[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Deep WMHs (DWMHs) and periventricular WMHs (PVWMHs) were graded separately from 0 to 3 according to the Fazekas scale, with the total Fazekas score calculated as the sum of these 2 parts (0\u0026thinsp;~\u0026thinsp;6)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eLacunes were defined as focal lesions of 3\u0026thinsp;~\u0026thinsp;15 mm in size, with the same signal characteristics as cerebrospinal fluid on all MRI sequences[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The number of lacunar lesions was recorded.\u003c/p\u003e\u003cp\u003eProgression in CSVD at the 12 months follow-up were evaluated using the following calculation formulas: ΔCMBs\u0026thinsp;=\u0026thinsp;Number of CMB lesions (12 month-baseline); ΔWMHs\u0026thinsp;=\u0026thinsp;Fazekas score (12 months།baseline); ΔLacunes\u0026thinsp;=\u0026thinsp;Number of lacunar lesions (12 months།baseline).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were presented in descriptive statistics, with mean and standard deviation (SD) or median with interquartile range (IQR) given for continuous variables, and percentages given for categorical variables. CSVD at baseline and 12 months were compared using the paired samples \u003cem\u003et\u003c/em\u003e-test or Wilcoxon signed-rank test. Multivariable linear regression model was used to identify the risk factors for CSVD progression, with ΔCMBs, ΔWMHs, and ΔLacunes designated as the dependent variables, respectively. Variables with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in the univariate analysis, as well as potential clinical risk factors for CSVD reported previously were incorporated into the multivariable linear regression model. A two-tailed \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. All analyses were performed with SPSS version 29.0 statistical software (SPSS Inc., Chicago, IL, USA).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline characteristics of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 113 patients were initially enrolled in this study. During the follow-up period, 10 patients expired and 11 patients withdrew for various reasons. Finally, 92 patients were finished the follow-up (Fig.1). Baseline characteristics of the remaining 92 patients are shown in Table 1, the mean age was (63.21\u0026plusmn;8.21)\u0026thinsp;years, 77.17% were men. Hypertension (91.30%), diabetes mellitus (66.30%) and CVD (61.96%) were the most common comorbidities. The median dialysis vintage was 33 (16, 113) months at baseline and mean weekly hemodialysis duration was (11.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78)\u0026thinsp;h. 57 patients received anticoagulation with heparin, 30 with low-molecular-weight heparin, and 5 without any anticoagulants. The dialysis blood flow rate was 220~300 mL/min, the dialysate flow was 500 mL/min, and dialysate temperature was 36.5\u0026deg;C. The mean MAP was (104.78\u0026plusmn;13.26) mmHg pre-hemodialysis and (112.52\u0026plusmn;13.34) mmHg post-hemodialysis. MFV decreased from (53.28\u0026plusmn;13.99) m/s pre- hemodialysis to (47.13\u0026plusmn;14.09) m/s at the end of hemodialysis, and the mean MFV reduction rate was (11.43\u0026plusmn;15.19) %. Hemodialysis-related clinical data are reported in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDemographic and clinical characteristics of participants at baseline\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"581\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eMan, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e71 (77.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e63.21 \u0026plusmn; 8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003ePrimary kidney disease,\u003cem\u003e\u0026nbsp;n\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eGlomerulonephritis\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e26\u0026nbsp;(28.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eDiabetes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e43\u0026nbsp;(46.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eVascular\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e14 (15.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eOther diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e9 (9.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eComorbidities, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e84\u0026nbsp;(91.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e61 (66.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eStroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e18 (19.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e57 (61.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eMedication, \u003cem\u003en\u003c/em\u003e (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eCCB\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e61\u0026nbsp;(66.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eACEI /ARB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e34\u0026nbsp;(36.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eB-blocker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e28\u0026nbsp;(30.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eNitrate\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e18\u0026nbsp;(19.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eAntiplatelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e29 (31.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eSmoking status, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e39 (42.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eAlcohol intake, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e14 (15.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eBMI\u0026nbsp;(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e23.58（20.35, 25.68）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eLaboratory examinations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eHGB\u0026nbsp;(g/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e113.35 \u0026plusmn; 13.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eALB\u0026nbsp;(g/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e36.54 \u0026plusmn; 3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eTC\u0026nbsp;(mmol/L)\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e3.41\u0026plusmn;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eTG\u0026nbsp;(\u0026nbsp;mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e1.47 \u0026plusmn; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eHDL-C\u0026nbsp;(mmol/L)\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e1.05\u0026plusmn;0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eLDL-C\u0026nbsp;(mmol/L)\u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e1.95\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003ehsCRP\u0026nbsp;(mg/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e4.43 (1.60, 8.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eCalcium\u0026nbsp;(mmol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e2.16\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003ePhosphate\u0026nbsp;(mmol/L)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e1.78\u0026plusmn;0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 329px;\"\u003e\n \u003cp\u003eiPTH\u0026nbsp;(pg/mL)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 253px;\"\u003e\n \u003cp\u003e160.90 (98.20, 278.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values are presented as mean \u0026plusmn; standard deviation for continuous variables with normal distribution, and median (interquartile range) for continuous variables with skewed distribution; n (%) for categorical variables\u003c/p\u003e\n\u003cp\u003eAbbreviations: CVD, cardiovascular disease. CCB, calcium channel blockers. ACEI, angiotensin converting enzyme inhibitors. ARB, angiotensin Ⅱ receptor blockers. BMI, body mass index. HGB, hemoglobin. ALB, albumin. TC, total cholesterol. TG, triglycerides. HDL-C, high-density lipoprotein cholesterol. LDL-C, low-density lipoprotein cholesterol. hsCRP, high-sensitivity C-reactive protein. iPTH, intact parathyroid hormone\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eHemodialysis-related clinical data of participants at baseline\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"581\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eValue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eDialysis vintage\u0026nbsp;(months)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e33 (16, 113)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eDialysis treatment time\u0026nbsp;(h/week)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e11.41\u0026plusmn;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eKt/V\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e1.19 \u0026plusmn; 0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eVascular access, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eCVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e71 (77.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eAVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e19 (20.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eAVG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e2 (2.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eAnticoagulation mode, \u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eUFH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e57 (61.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eLMWH\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e30 (32.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eNo anticoagulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e5 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003ePre-hemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eSBP\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e156.75\u0026plusmn;20.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eDBP\u0026nbsp;(mmHg)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e78.79\u0026plusmn;12.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eMAP\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e104.78 \u0026plusmn; 13.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eMFV\u0026nbsp;(cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e53.28\u0026plusmn;13.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003ePost-hemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eSBP\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e168.39\u0026plusmn;17.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eDBP\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e84.59\u0026plusmn;14.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eMAP\u0026nbsp;(mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e112.52\u0026plusmn;13.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eMFV\u0026nbsp;(cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e47.13\u0026plusmn;14.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eMVF reduction rate\u0026nbsp;(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e11.43 \u0026plusmn; 15.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 382px;\"\u003e\n \u003cp\u003eUFR\u0026nbsp;(mL/h)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e428.86 \u0026plusmn; 233.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values are presented as mean \u0026plusmn; standard deviation for continuous variables with normal distribution, and median (interquartile range) for continuous variables with skewed distribution; n (%) for categorical variables\u003c/p\u003e\n\u003cp\u003eAbbreviations: Kt/V, urea reduction index. CVC, central venous catheter. AVF, autologous arteriovenous fistula. AVG, arteriovenous graft. UFH, unfractionated heparin. LMWH, low molecular weight heparin. SBP, systolic blood pressure. DBP, diastolic blood pressure. MAP, mean arterial pressure. MVF, mean flow velocity. UFR, ultrafiltration rate\u003c/p\u003e\n\u003ch3\u003eCSVD at baseline and 12\u0026thinsp;months follow-up\u003c/h3\u003e\n\u003cp\u003eAt baseline, among the 92 MHD patients,\u0026nbsp;56 (60.87%) had CSVD, including 33 (35.87%) with CMBs, 38 (41.30%) with WMHs and 30 (32.61%) with Lacunes. After 12 months of follow-up, 63 (68.48%)\u0026nbsp;had CSVD, including 38 (41.30 %) with CMBs, 40 (43.48%) with WMHs and 33 (35.87%) with Lacunes.\u003c/p\u003e\n\u003cp\u003eCompared with the brain MRI data at baseline, the counts of CMBs and lacunar, and Fazekas scores of WMHs all significantly increased at 12 months (all \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalues \u0026lt; 0.05). The results are shown in Table 3, and representative MRI images of CSVD are illustrated in Fig.2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eComparison of CSVD imaging markers at baseline and 12-month follow-up\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"581\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12 Months\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et/Z\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ev\u003c/strong\u003e\u003cstrong\u003ealue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-v\u003c/strong\u003e\u003cstrong\u003ealue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eCBMs (count)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTotal Count\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e17.00 (5.00, 36.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e23.00 (12.00, 44.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-7.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eLobe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e4.50 (1.00, 10.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e7.50 (2.00, 14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-6.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDeep\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e4.00 (2.00, 14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e7.50 (2.00, 18.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-5.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eInfratentorial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e5.00 (1.00, 9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e8.00 (2.00, 12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-6.933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eWMHs (Fazekas Score)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eTotal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e3.623\u0026plusmn;1.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e3.88\u0026plusmn;1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-4.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003ePeriventricular White Matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e2.03\u0026plusmn;0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e2.22\u0026plusmn;0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-4.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eDeep White Matter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1.53\u0026plusmn;0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e1.68\u0026plusmn;0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-2.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 193px;\"\u003e\n \u003cp\u003eLacunes (Count)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 129px;\"\u003e\n \u003cp\u003e1.00 (0.00, 2.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 137px;\"\u003e\n \u003cp\u003e2.50 (1.00, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e-6.746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Values are presented as mean \u0026plusmn; standard deviation for continuous variables with normal distribution, and median (interquartile range) for continuous variables with skewed distribution\u003c/p\u003e\n\u003cp\u003eAbbreviations: CMBs, cerebral microbleeds. WMHs, white matter hyperintensities\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociated\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ef\u003c/strong\u003e\u003cstrong\u003eactors of CSVD progression in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMHD\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiple linear regression analysis showed several significant associations with CSVD progression (Table 4). Longer dialysis vintage, higher MVF reduction rate, and elevated hsCRP levels were significantly associated with \u0026Delta;CMBs (\u003cem\u003eB\u003c/em\u003e = 0.065, 95% \u003cem\u003eCI\u003c/em\u003e: 0.021\u0026ndash;0.109, \u003cem\u003ep\u003c/em\u003e = 0.004;\u0026nbsp;\u003cem\u003eB\u003c/em\u003e =\u0026nbsp;19.274, 95% \u003cem\u003eCI\u003c/em\u003e:\u0026nbsp;0.379-38.168, \u003cem\u003ep\u003c/em\u003e = 0.046;\u0026nbsp;\u003cem\u003eB\u003c/em\u003e = 0.311, 95% \u003cem\u003eCI\u003c/em\u003e: 0.068\u0026ndash;0.554, \u003cem\u003ep\u003c/em\u003e = 0.013). Aging and increased\u0026nbsp;MVF reduction rate\u0026nbsp;were\u0026nbsp;associated with \u0026Delta;WMHs (\u003cem\u003eB\u003c/em\u003e = 0.023, 95% \u003cem\u003eCI\u003c/em\u003e: 0.006\u0026ndash;0.040, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.010;\u0026nbsp;\u003cem\u003eB\u003c/em\u003e =\u0026nbsp;1.352, 95% \u003cem\u003eCI\u003c/em\u003e:\u003cem\u003e\u0026nbsp;\u003c/em\u003e0.156-1.636, \u003cem\u003ep\u003c/em\u003e = 0.013). In addition, smoking\u0026nbsp;status\u0026nbsp;and diabetes mellitus\u0026nbsp;were significant risk factors for\u0026nbsp;\u0026Delta;Lacunes (\u003cem\u003eB\u003c/em\u003e =0.896, 95% \u003cem\u003eCI\u003c/em\u003e:\u0026nbsp;0.156-1.636, \u003cem\u003ep\u003c/em\u003e =\u0026nbsp;0.018;\u0026nbsp;\u003cem\u003eB\u003c/em\u003e =\u0026nbsp;1.230, 95% \u003cem\u003eCI\u003c/em\u003e:\u0026nbsp;0.414-2.045, \u003cem\u003ep\u003c/em\u003e = 0.004).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u0026nbsp;\u003c/strong\u003eMultivariable linear regression analysis of risk factors for CSVD progression\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% \u003cem\u003eCI\u003c/em\u003e for \u003cem\u003eB\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e-value\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;CMBs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eDialysis vintage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.021-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eMVF reduction rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e19.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.379-38.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003ehsCRP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.068-0.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;WMHs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.006-0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eMVF reduction rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.190-2.514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026Delta;Lacunes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eSmoking status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.896\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.156-1.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 194px;\"\u003e\n \u003cp\u003eDiabetes mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e1.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e0.414-2.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn multivariable linear regression analysis for \u0026Delta;WMHs and \u0026Delta;Lacunes, age, gender, hypertension, diabetes mellitus, smoking, dialysis vintage, MVF reduction rate, UFR, HGB, ALB, and hsCRP were adjusted; for \u0026Delta;CMBs, anticoagulation mode, TG were additionally adjusted on the basis of the aforementioned covariates\u003c/p\u003e\n\u003cp\u003eAbbreviations: CMBs, cerebral microbleeds. WMHs, white matter hyperintensities. MVF, mean flow velocity. UFR, ultrafiltration rate. HGB, hemoglobin. ALB, albumin. hsCRP, high-sensitivity C-reactive protein. TG, triglycerides\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCSVD is highly prevalent among MHD patients and accompanied by poor clinical prognosis[11]. However, the etiology is complex and underlying mechanisms remain unclear[2]. Furthermore, longitudinal studies exploring CSVD progression in this cohort remain scarce and are limited by small sample sizes. In this 12-month prospective follow-up study of MHD patients, we observed significant progression in the numbers of CMBs and Lacunes, as well as the Fazekas score for WMHs, compared with baseline. Further multivariate linear regression analysis shown different risk factors for the progression of CSVD subtypes. Longer dialysis vintage, higher MFV reduction rate, and elevated hsCRP levels were significantly associated with CMBs progression. Aging and increased MFV reduction rate were independent risk factors for WMHs progression; while smoking and diabetes mellitus were significantly associated with Lacunes progression. These findings help further explain the potential mechanisms underlying CSVD in MHD patients, and allow for more careful monitoring and targeted intervention measures.\u003c/p\u003e\n\u003cp\u003eWMHs prevalence increases with age, it can be found in 20% of adults in their sixties and in up to 94% in the population of octogenarians[19]. In present study,\u0026nbsp;aging\u0026nbsp;remained an independent risk factor for WMHs progression\u0026nbsp;among MHD patients, which is consistent with findings in the general population[10].\u0026nbsp;Lacunes result from the occlusion of small perforating arteries.\u0026nbsp;Diabetes mellitus and\u0026nbsp;smoking\u0026nbsp;have been established as important risk factors[20].\u0026nbsp;Diabetes accelerates atherosclerosis and thrombosis in small penetrating arteries by promoting the formation of advanced glycation end products and increasing blood viscosity[21-23]. Smoking, in turn, impairs vascular endothelial function through vasospasm and oxidative stress[23, 24]. Collectively, these processes raise the risk of occlusion in deep cerebral small arteries in patients undergoing maintenance hemodialysis, thereby facilitating the development of Lacunes[23]. These findings emphasize that tight glycemic control and smoking cessation represent key interventions for preventing Lacunes in this patient population.\u003c/p\u003e\n\u003cp\u003ehsCRP is a sensitive marker of chronic inflammation[25]. The present study showed that elevated hsCRP\u0026nbsp;levels\u0026nbsp;are an independent risk factor for CMBs progression in MHD patients. MHD patients often present with\u0026nbsp;persistent systemic low‑grade inflammation due to multiple factors, such as uremic toxin stimulation, poor biocompatibility of dialysis membranes, and repeated infections etc[26-28]. The chronic inflammation induces a state of endothelial cell dysfunction impairing cerebral blood flow regulation and breaking the blood brain barrier[29]. Combined with hemodynamic fluctuations during dialysis, it results in neuronal injury and CSVD clinical manifestations.\u003c/p\u003e\n\u003cp\u003eNotably,\u0026nbsp;the intradialytic MFV reduction rate is a risk factor for the progression of both CMBs and WMHs. Previous studies have demonstrated evidence of decreased CBF in MHD patients during hemodialysis treatment, but whether such short-term CBF changes are associated with CSVD progression remains unclear. Kalantari et al.[30]\u0026nbsp;used pseudo continuous arterial spin labeling magnetic resonance imaging (pCASL-MRI) to measure CBF in CSVD patients, and the results showed a significant negative correlation between CBF and the Fazekas score of WMHs. However, MHD patients were not included in the study cohort, and thus could not define the specific role of dialysis-related CBF changes. Findlay et al.[12]reported that patients undergoing hemodialysis experience transient decline in CBF. The percentage of decline in MFV correlated significantly with progression of WMHs burden in patients on continued dialysis, but not in transplanted patients. Li et al.[31]found that\u0026nbsp;CBF in hemodialysis patients could manifest as both increased and decreased,\u0026nbsp;compared with the increased CBF group, WMH in patients with decreased CBF developed severely with prolongation of hemodialysis duration. These findings support the impact of reduced CBF on WMHs in MHD patients. However, they did not analyze the association between the rate of CBF decline and the progression of WMHs, nor did they explore other subtypes of CSVD such as CMBs. The present study analyzed the association between the rate of MFV reduction and multiple CSVD subtypes. After adjusting for confounding factors, the intradialytic MFV reduction rate is an independent risk factor for the progression of both WMHs and CMBs. CBF reduction during hemodialysis may further exacerbate the hypoxic state of brain tissue, and cerebral hypoperfusion can induce endothelial cell injury and blood-brain barrier dysfunction, thereby promoting the progression of CMBs and WMHs[32]. This finding indicates that maintaining stable cerebral hemodynamics during hemodialysis may be crucial for preventing CSVD progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present prospective study identified risk factors associated with CSVD progression in patients undergoing maintenance hemodialysis. However, it still has several limitations. First, it is a single-center study, which may affect the generalizability and extrapolation of the results. Second, some patients were lost to follow-up, which may introduce selection bias. Due to these limitations, further verification through multi-center studies with large sample sizes is required.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, CSVD progression in MHD patients is driven by the combined effects of traditional vascular risk factors and dialysis-related factors. Clinically, individualized strategies should be developed, with enhanced monitoring and intervention of high-risk factors to reduce the risk of adverse outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eCSVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eCerebral small vessel disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eMHD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eMaintenance hemodialysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eTCD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eTranscranial Doppler\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eMFV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eMean flow velocity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eMagnetic resonance imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eCMBs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eCerebral microbleeds\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eWMHs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eWhite matter hyperintensities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eCBF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eCerebral blood flow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eCardiovascular disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eUFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eUltrafiltration rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eDBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eMCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eMiddle cerebral artery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eMAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eMean arterial pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eDWMHs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eDeep white matter hyperintensities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003ePVWMHs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003ePeriventricular white matter hyperintensities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eCVC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eCentral venous catheter\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eAVF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eAutologous arteriovenous fistula\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eAVG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eArteriovenous graft\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eUFH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eUnfractionated heparin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eLMWH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eLow molecular weight heparin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eInterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eHGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eHemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eAlbumin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003eiPTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eIntact parathyroid hormone\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 299px;\"\u003e\n \u003cp\u003ehsCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 297px;\"\u003e\n \u003cp\u003eHigh-sensitivity C-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXLZ: Data collection, analysis, manuscript writing. YDG: Statistical data analysis. CXZ, ZHS, MJ, JYS, XYZ, XYS: \u0026nbsp; Data collection. YL, YD, YDG: Study design, manuscript revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCapital\u0026rsquo;s Funds for Health Improvement and Research (CFH 2022-2-2081);\u0026nbsp;Beijing Municipal Science \u0026amp; Technology Commission national key research and development plan matching project (Z161100002616005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in the current study are not publicly available to ensure the privacy of research participants and comply with the regulations of the ethics approval. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study has been approved by the Ethics Committee of Beijing Shijitan Hospital, Capital Medical University\u0026nbsp;[Ethics Approval Number: sjtkyll-lx-2022-078]. Informed consent was obtained from all study participants.\u0026nbsp;The study was conducted following the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarkus HS, Joutel A. The pathogenesis of cerebral small vessel disease and vascular cognitive impairment. Physiol Rev. 2025;105(3):1075-171.\u003c/li\u003e\n\u003cli\u003eDupr\u0026eacute; N, Drieu A, Joutel A. Pathophysiology of cerebral small vessel disease: a journey through recent discoveries. J Clin Invest. 2024;134(10):e172841.\u003c/li\u003e\n\u003cli\u003eChojdak-Łukasiewicz J, Dziadkowiak E, Zimny A,et al. Cerebral small vessel disease: A review. Adv Clin Exp Med. 2021;30(3):349-56.\u003c/li\u003e\n\u003cli\u003eHainsworth AH, Markus HS, Schneider JA. Cerebral Small Vessel Disease, Hypertension, and Vascular Contributions to Cognitive Impairment and Dementia. Hypertension. 2024;81(1):75-86.\u003c/li\u003e\n\u003cli\u003eDuering M, Biessels GJ, Brodtmann A, et al. Neuroimaging standards for research into small vessel disease-advances since 2013. Lancet Neurol. 2023;22(7):602-18.\u003c/li\u003e\n\u003cli\u003eWardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013;12(8):822-38.\u003c/li\u003e\n\u003cli\u003eCannistraro RJ, Badi M, Eidelman BH, et al. CNS small vessel disease: A clinical review. Neurology. 2019;92(24):1146-56.\u003c/li\u003e\n\u003cli\u003eLamar M, Leurgans S, Kapasi A, et al. Complex Profiles of Cerebrovascular Disease Pathologies in the Aging Brain and Their Relationship With Cognitive Decline. Stroke. 2022;53(1):218-27.\u003c/li\u003e\n\u003cli\u003eHairus S MN, Srinivasan A, Shaik Mohideen SA, et al. Cerebral Small-Vessel Disease Markers on Magnetic Resonance Imaging as Predictors of Recurrent Vascular Events and Death in Ischemic Stroke: A Systematic Review and Meta-Analysis. J Am Heart Assoc. 2025;14(17):e040082.\u003c/li\u003e\n\u003cli\u003eNaganuma T, Takemoto Y. Asymptomatic Cerebrovascular Disease in Dialysis Patients. Contrib Nephrol. 2018;196:22-6.\u003c/li\u003e\n\u003cli\u003eZheng K, Zhou Y, Qian Y, et al. Increased Premature Cerebral Small Vessel Diseases in Dialysis Patients: A Retrospective Cross-Sectional Study. Nephron. 2021;145(4):330-41.\u003c/li\u003e\n\u003cli\u003eFindlay MD, Dawson J, Dickie DA,et al. Investigating the Relationship between Cerebral Blood Flow and Cognitive Function in Hemodialysis Patients. J Am Soc Nephrol. 2019;30(1):147-58.\u003c/li\u003e\n\u003cli\u003eQian Y, Zheng K, Wang H, et al. Cerebral microbleeds and their influence on cognitive impairment in Dialysis patients. Brain Imaging Behav. 2021;15(1):85-95.\u003c/li\u003e\n\u003cli\u003eAngermann S, G\u0026uuml;nthner R, Hanssen H, et al. Cognitive impairment and microvascular function in end-stage renal disease. Int J Methods Psychiatr Res. 2022;31(2):e1909.\u003c/li\u003e\n\u003cli\u003eBi R, Wei Y, Li P, et al. Associations of Cerebral Small Vessel Disease and Chronic Kidney Disease in Patients With Acute Ischemic Stroke. J Am Heart Assoc. 2025;14(9):e038711.\u003c/li\u003e\n\u003cli\u003eLau WL, Huisa BN, Fisher M. The Cerebrovascular-Chronic Kidney Disease Connection: Perspectives and Mechanisms. Transl Stroke Res. 2017;8(1):67-76.\u003c/li\u003e\n\u003cli\u003eShi L, Zheng K, Qian Y, et al. Effect of ultrafiltration on cerebral small-vessel disease and related outcomes in hemodialysis. Clin Kidney J. 2023;16(7):1139-48.\u003c/li\u003e\n\u003cli\u003eFazekas F, Chawluk JB, Alavi A, et al. MR signal abnormalities at 1.5 T in Alzheimer\u0026apos;s dementia and normal aging. AJR Am J Roentgenol. 1987;149(2):351-6.\u003c/li\u003e\n\u003cli\u003eGarde E, Mortensen EL, Krabbe K, et al. Relation between age-related decline in intelligence and cerebral white-matter hyperintensities in healthy octogenarians: a longitudinal study. Lancet. 2000;356(9230):628-34.\u003c/li\u003e\n\u003cli\u003eLitak J, Mazurek M, Kulesza B, et al. Cerebral Small Vessel Disease. Int J Mol Sci. 2020;21(24):9729.\u003c/li\u003e\n\u003cli\u003ePaneni F, Beckman JA, Creager MA,et al. Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part I. Eur Heart J. 2013;34(31):2436-43.\u003c/li\u003e\n\u003cli\u003eTwarda-Clapa A, Olczak A, Białkowska AM, et al. Advanced Glycation End-Products (AGEs): Formation, Chemistry, Classification, Receptors, and Diseases Related to AGEs. Cells. 2022;11(8):1312.\u003c/li\u003e\n\u003cli\u003eWang Z, Chen Q, Chen J, et al. Risk factors of cerebral small vessel disease: A systematic review and meta-analysis. Medicine (Baltimore). 2021;100(51):e28229.\u003c/li\u003e\n\u003cli\u003eFetterman JL, Keith RJ, Palmisano JN, et al. Alterations in Vascular Function Associated With the Use of Combustible and Electronic Cigarettes. J Am Heart Assoc. 2020;9(9):e014570.\u003c/li\u003e\n\u003cli\u003eQueissner R, Fellendorf FT, Dalkner N, et al. The influence of chronic inflammation on the illnesscourse of bipolar disorder: A longitudinal study. J Psychiatr Res. 2024;174:258-62.\u003c/li\u003e\n\u003cli\u003eCobo G, Lindholm B, Stenvinkel P. Chronic inflammation in end-stage renal disease and dialysis. Nephrol Dial Transplant. 2018;33(suppl_3):iii35-35iii40.\u003c/li\u003e\n\u003cli\u003eMukai H, Villafuerte H, Qureshi AR, et al. Serum albumin, inflammation, and nutrition in end-stage renal disease: C-reactive protein is needed for optimal assessment. Semin Dial. 2018;31(5):435-9.\u003c/li\u003e\n\u003cli\u003eSilva RE, Santos EC, Justino P, et al. Cytokines and chemokines systemic levels are related to dialysis adequacy and creatinine clearance in patients with end-stage renal disease undergoing hemodialysis. Int Immunopharmacol. 2021;100:108154.\u003c/li\u003e\n\u003cli\u003eHannawi Y. Cerebral Small Vessel Disease: a Review of the Pathophysiological Mechanisms. Transl Stroke Res. 2024;15(6):1050-69.\u003c/li\u003e\n\u003cli\u003eKalantari S, Soltani M, Maghbooli M, et al. Cerebral blood flow alterations measured by ASL-MRI as a predictor of vascular dementia in small vessel ischemic disease. Radiologia (Engl Ed). 2025;67(1):28-37.\u003c/li\u003e\n\u003cli\u003eLi M, Yang W, Song L, et al. Association between white matter hyperintensities and altered cerebral blood flow in maintenance hemodialysis patients: a longitudinal study. BMC Nephrol. 2024;25(1):33.\u003c/li\u003e\n\u003cli\u003eMcIntyre CW. Update on Hemodialysis-Induced Multiorgan Ischemia: Brains and Beyond. J Am Soc Nephrol. 2024;35(5):653-64.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Renal dialysis, Cerebral small vessel disease, Cerebrovascular circulation, Cerebral blood flow","lastPublishedDoi":"10.21203/rs.3.rs-9536460/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9536460/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eCerebral small vessel disease (CSVD) is a major vascular contributor to dementia, cognitive decline, gait impairment, mood disturbance and stroke. Maintenance hemodialysis (MHD) patients have a high CSVD prevalence with poor prognosis. However, the dynamic progressive features and associated risk factors of CSVD in MHD patients remain unclear. We aimed to explore the progression of CSVD subtypes and their associated risk factors in MHD patients.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis prospective cohort study enrolled MHD patients from January to June 2023 at Beijing Shijitan Hospital. Clinical and dialysis-related data were collected. Transcranial Doppler (TCD) was used to monitor the mean flow velocity (MFV) of the middle cerebral artery during dialysis, and the MFV reduction rate during dialysis was calculated. The cerebral microbleeds (CMBs), white matter hyperintensities (WMHs), and Lacunes on brain magnetic resonance imaging (MRI) were assessed at baseline and at the 12-month follow-up. Multivariate linear regression model was used to analyze the risk factors associated with the progression of CSVD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 92 MHD patients were included in this study, with a mean age of (63.21\u0026thinsp;\u0026plusmn;\u0026thinsp;8.21) years (40\u0026ndash;85 years old) and 71 males (77.17%). Compared with baseline, significant progression was observed in CMBs, WMHs and Lacunes at the 12-month follow-up (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate linear regression analysis showed that longer dialysis vintage, higher MVF reduction rate, and elevated hsCRP levels were significantly associated with CMBs progression (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.065, 95% \u003cem\u003eCI\u003c/em\u003e: 0.021\u0026ndash;0.109, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004; \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19.274, 95% \u003cem\u003eCI\u003c/em\u003e: 0.379\u0026ndash;38.168, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046; \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.311, 95% \u003cem\u003eCI\u003c/em\u003e: 0.068\u0026ndash;0.554, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). Aging and increased MVF reduction rate were associated with WMHs progression (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023, 95% \u003cem\u003eCI\u003c/em\u003e: 0.006\u0026ndash;0.040, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010; \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.352, 95% \u003cem\u003eCI\u003c/em\u003e: 0.156\u0026ndash;1.636, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013). In addition, smoking status and diabetes mellitus were significantly linked to lacunars progression (\u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.896, 95% \u003cem\u003eCI\u003c/em\u003e: 0.156\u0026ndash;1.636, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018; \u003cem\u003eB\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.230, 95% \u003cem\u003eCI\u003c/em\u003e: 0.414\u0026ndash;2.045, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn addition to age, dialysis vintage, diabetes mellitus and smoking, the reduction in cerebral blood flow during hemodialysis is an independent risk factor for the progression of CSVD in MHD patients.\u003c/p\u003e","manuscriptTitle":"Progression of cerebral small vessel disease in maintenance hemodialysis patients: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-12 16:55:10","doi":"10.21203/rs.3.rs-9536460/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-05-04T12:52:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T09:55:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T08:38:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T08:38:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2026-04-27T05:05:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"412f1e0f-0ad7-4bfe-80db-41e2fc36f8d7","owner":[],"postedDate":"May 12th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"4","date":"2026-05-04T12:52:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-05-04T09:55:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-30T08:38:55+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-30T08:38:29+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T16:55:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-12 16:55:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9536460","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9536460","identity":"rs-9536460","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}