Hemodialysis and its effects on glymphatic system function in end-stage kidney disease patients

Introduction

Chronic kidney disease (CKD) is a growing global health concern, affecting millions of individuals worldwide. With an incidence rate of approximately 24.2 per 100,000 people and an annual growth rate of around 8%, CKD represents a significant public health challenge (1). End-stage kidney disease (ESKD), the final stage of CKD, is typically diagnosed when the glomerular filtration rate (GFR) falls below 15 mL/min/1.73 m². One of the major complications of ESKD is cognitive impairment, with prevalence rates ranging from approximately 16% to 38%, nearly three times higher than those observed in the age-matched general population (2). Although the mechanisms underlying cognitive impairment in ESKD are not fully understood, growing evidence suggests that disruptions in brain waste clearance pathways, such as the glymphatic system, may contribute to this phenomenon (3).

The glymphatic system, a type of cerebrospinal fluid (CSF) transport system, is employed to remove waste from the brain (4,5). The glymphatic system facilitates the movement of CSF from the arterial perivascular spaces into the brain parenchyma through aquaporin-4 (AQP4) channels expressed in astrocytic endfeet. From there, interstitial fluid and waste products are transported along venous perivascular routes and cleared through meningeal and cervical lymphatic vessels (6). Currently, methods for evaluating the glymphatic system are relatively limited. Magnetic resonance imaging (MRI) with intrathecal gadolinium-based contrast agents is the most representative technique for assessing glymphatic function (7). However, its clinical application is limited by its invasiveness and procedural complexity, particularly due to the contraindication of contrast agents in patients with renal impairment. Recently, a diffusion tensor image analysis along the perivascular space (DTI-ALPS) index has been developed, which utilizes non-invasive diffusion imaging to assess glymphatic function (8). The DTI-ALPS method measures water diffusion along perivascular spaces, reflecting the efficiency of CSF and interstitial fluid exchange, which is a key component of glymphatic clearance. A higher ALPS index may reflect more efficient glymphatic system function, whereas a lower index may suggest reduced clearance efficiency. Multiple studies have demonstrated that a reduced ALPS index is negatively correlated with diseases such as Alzheimer’s disease, Parkinson’s disease, epilepsy, stroke, and obstructive sleep apnea–hypopnea syndrome, where glymphatic dysfunction contributes to waste accumulation and neurodegeneration (9-13).

Studies have demonstrated that both early and end stages of CKD are linked with abnormalities in the glymphatic system (14,15). This glymphatic dysfunction is associated with the cognitive impairment observed in CKD patients (16). However, there is limited research on whether short-term improvements in glymphatic function occur following hemodialysis in ESKD patients.

We hypothesized that acute hemodynamic shifts and rapid clearance of small water-soluble uremic toxins during hemodialysis might transiently modulate glymphatic function. Therefore, the aim of this study was to assess the glymphatic system in pre- and post-hemodialysis ESKD patients as well as compare them to healthy controls (HCs) using the DTI-ALPS method. This non-invasive and sensitive technique allows for a detailed assessment of glymphatic function and provides insight into the potential short-term effects of hemodialysis on this system. The findings of this study may provide valuable insights into the mechanisms underlying cognitive impairment in ESKD patients. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-74/rc).

Methods

Participants

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendment and was approved by the Ethics Committee of Tongren People’s Hospital (No. 202304). Informed consent was provided by all individual participants. We enrolled 25 patients with ESKD from Tongren People’s Hospital and recruited 27 age- and gender-matched HCs from November 2023 to June 2024. The study flow chart is illustrated in Figure 1.

Figure 1 Flow diagram of the study sample. ESKD, end-stage kidney disease; HCs, healthy controls.

The inclusion criteria for this study were as follows: adult ESKD patients aged 18–75 years, undergoing regular hemodialysis with a stable clinical condition, no acute illness or infection within the past month, and the ability to undergo MRI scans both before and after hemodialysis. Age- and sex-matched HCs with normal renal function, no history of kidney disease or chronic conditions affecting brain health, and no recent infections or acute illnesses within the past month were also recruited. Exclusion criteria for all participants included neurological or psychiatric disorders (e.g., stroke, dementia, major depressive disorder) that could impact brain function, a history of traumatic brain injury, MRI contraindications (e.g., metal implants, claustrophobia, pacemakers), pregnancy or breastfeeding, and current alcohol or substance abuse. Additional exclusion criteria for ESKD patients included unstable medical conditions, such as poorly controlled hypertension or diabetes, and recent changes in hemodialysis regimen within the past month.

Laboratory data collection and analysis

Blood samples were collected from ESKD patients at two time points to evaluate a panel of laboratory variables: (I) immediately before hemodialysis (pre-hemodialysis samples) and (II) immediately after completion of the hemodialysis session (post-hemodialysis samples). Pre-hemodialysis samples were obtained from the dialysis tubing immediately following the initiation of hemodialysis sessions to ensure consistency in sampling conditions.

All laboratory analyses were performed using standardized, automated analytical platforms following manufacturer protocols. Biochemical assays, including tests for renal function [blood urea nitrogen (BUN), creatinine], liver function [aspartate aminotransferase (AST), alanine aminotransferase (ALT)], electrolytes (potassium, sodium, chloride, calcium), iron metabolism (iron, ferritin), lipid profile (total cholesterol), and nutritional markers (albumin, total protein, prealbumin), were conducted using the Beckman Coulter AU5800 automated biochemistry analyzer (Beckman Coulter, Brea, CA, USA). Hematological parameters (hemoglobin, hematocrit) were measured using the Mindray BC-7500[NR]CRP automated hematology analyzer (Mindray, Shenzhen, China). Parathyroid hormone (PTH) levels were measured using an electrochemiluminescence immunoassay (ECLIA) on a Roche Cobas e601 analyzer (Roche Diagnostics, Basel, Switzerland). The estimated glomerular filtration rate (eGFR) was calculated based on serum creatinine levels using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation (17).

To ensure analytical accuracy, all blood samples were processed within 2 hours of collection. Samples were transported under controlled conditions to the laboratory, where they were centrifuged, and plasma or serum was separated as required for specific assays. Quality control measures were implemented following the manufacturers’ guidelines, and all instruments were calibrated prior to analysis.

MRI acquisition

MRI data were acquired using a Philips Ingenia CX 3.0T scanner (Philips, Amsterdam, Netherlands). During MRI scanning, participants were instructed to remain still in a supine position with their eyes closed, maintain steady breathing, stay awake, and refrain from engaging in deliberate thought. To reduce noise, earplugs or headphones were provided, and foam cushions were placed on either side of the head to minimize head movement.

HCs underwent a single MRI scan, whereas ESKD patients underwent two scans: one before and one after hemodialysis. The pre-hemodialysis MRI scan was performed 2–24 hours prior to hemodialysis, and the post-hemodialysis MRI scan occurred 24–48 hours afterward. DTI data were acquired using a single-shot echo planar imaging (EPI) sequence with the following parameters: repetition time/echo time (TR/TE) =9,700/74.2 ms, b-values =0 and 1,000 s/mm², 32 diffusion directions, field of view (FOV) =256 mm × 256 mm, matrix size =128×128, 48 slices, 3 mm slice thickness without an intersection gap, and in-plane resolution 2×2 mm².

Participants with excessive motion or artifacts were excluded. In addition, conventional MRI scans, including axial and sagittal T2, as well as axial T2 fluid-attenuated inversion recovery (FLAIR), were performed to rule out organic brain abnormalities.

DTI-ALPS processing

DTI-ALPS data processing was conducted following the methodology outlined in our previous study (9). Digital Imaging and Communications in Medicine (DICOM) images were initially converted to Neuroimaging Informatics Technology Initiative (NIfTI) format using the MRIcron tool (https://www.nitrc.org/projects/mricron). During this conversion, the corresponding b-vector and b-value files, essential for DTI-ALPS processing, were generated. Preprocessing of the DTI images was conducted using the FSL pipeline in version 6.0 of the FMRIB Software Library (FSL; https://fsl.fmrib.ox.ac.uk/fsl) (18,19).

The preprocessing workflow included several key steps. Artifact correction was performed using the Marchenko-Pastur Principal Component (MP-PCA) denoising algorithm and Gibbs-unringing, implemented with MRtrix3 commands “dwidenoise” and “mrdegibbs”. Eddy current and motion corrections were then applied using the “eddy” command in FSL. Head motion was quantified using the mean absolute intervolume displacement (MAID) (20,21), calculated as the average Euclidean distance of each volume’s translational displacement relative to the first volume, derived from the transformation matrices in eddy correction outputs. Skull stripping was conducted to remove non-brain tissue from the images, followed by the generation of fractional anisotropy (FA) and diffusivity maps along x-, y-, and z-axes using the FSL function “dtifit”.

Next, each participant’s FA map was co-registered to the Johns Hopkins University-International Consortium for Brain Mapping (JHU-ICBM-FA) template using FSL’s “flirt” command, and the transformation matrix was applied to the corresponding diffusivity maps. The projection and association fibers at the level of the lateral ventricle body, specifically the superior corona radiata (SCR) and the superior longitudinal fasciculus (SLF), were identified based on the JHU-ICBM-DTI-81-white-matter labeled atlas. Regions of interest (ROIs) were automatically defined as 5 mm-diameter spheres within the bilateral SLF and SCR on all participants’ diffusivity maps, as illustrated in Figure 2. The central coordinates of these ROIs, referenced to the JHU-ICBM-FA template, were as follows: left SCR (116, 110, 99), left SLF (128, 110, 99), right SCR (64, 110, 99), and right SLF (51, 110, 99) (19).

Figure 2 Schematic illustration of the principle behind DTI-ALPS. DTI-ALPS, diffusion tensor image analysis along the perivascular space.

Diffusivity values (D_xx, D_yy, and D_zz) for the bilateral SLF and SCR were automatically extracted for the ALPS index calculation. Automatic extraction was achieved by registering each participant’s FA map to the JHU-ICBM-FA template and programmatically placing spherical ROIs at predefined template-based coordinates for the SLF and SCR, ensuring consistent and reproducible ROI positioning across participants. Manual verification was performed to ensure the accuracy of the registration and ROI placement for each participant.

The ALPS index was calculated as the ratio of the mean x-axis diffusivity in the projection fibers regions (D_xxproj) and the association fibers regions (D_xxassoc) to the mean of the y-axis diffusivity in the projection fibers regions (D_yyproj) and the z-axis diffusivity in the association fibers regions (D_zzassoc), as shown in Eq. [1] (8).

ALPS-index=mean(Dxxproj,Dxxassoc)mean(Dyyproj,Dzzassoc)[1]

The final ALPS index was obtained by averaging the bilateral ALPS index values.

Sensitivity analysis

To address potential confounding effects of comorbidities on the ALPS index, we conducted a sensitivity analysis excluding ESKD patients with documented cardiovascular or systemic comorbidities. This subgroup analysis aimed to isolate the impact of kidney dysfunction itself on the ALPS index. Of the 25 ESKD patients, 13 had ≥1 comorbidity and were excluded, leaving 12 “comorbidity-free” ESKD patients for comparison with the original HC group (HCs, n=27). Statistical methods remained consistent with the primary analysis.

Statistical analysis

Statistical comparisons for group differences were conducted by applying the chi-square test for categorical variables. For continuous variables, the Shapiro-Wilk test was first used to assess the normality of distributions in each group. When both groups met the assumption of normality, Levene’s test was then performed to evaluate the homogeneity of variances. Based on the results, an appropriate statistical test was selected. Specifically, if both normality and homogeneity of variances were confirmed, an independent two-sample t-test was used. If normality was met, but variances were unequal, Welch’s t-test was applied to account for the variance difference. If either group did not satisfy the normality assumption, the Mann-Whitney U test was employed as a non-parametric alternative. In the analysis of ALPS index differences between groups, analysis of variance (ANOVA) was used with sex and age included as covariates to adjust for their potential effects.

Paired comparisons for measurements before and after hemodialysis were analyzed by first assessing the normality of paired differences with the Shapiro-Wilk test. If the paired differences followed a normal distribution, a paired t-test was used to compare means between samples; if normality was not met, the Wilcoxon signed-rank test was employed as a non-parametric alternative, allowing for deviations from normality.

The correlation between the ALPS index and laboratory metrics and clinical data was evaluated by using either Pearson or Spearman correlation coefficients, depending on distribution characteristics. The normality of both the ALPS index and laboratory metrics was first examined with the Shapiro-Wilk test. For pairs of variables with normal distributions, the Pearson correlation coefficient was used to measure the strength and direction of their linear relationship. For cases where one or both variables did not meet the normality assumption, the Spearman rank correlation coefficient was employed to assess the strength and direction of their monotonic relationship.

All statistical analyses were conducted using R software (version 4.3.2, R Foundation for Statistical Computing, Vienna, Austria) and GraphPad Prism (version 10.1.2, GraphPad Software, San Diego, CA, USA). A two-tailed P value of less than 0.05 was considered statistically significant. The false discovery rate (FDR) correction method was applied for multiple comparisons.

Results

Demographics and clinical characteristics

A total of 52 participants were included in this study, comprising 25 ESKD patients (mean age 49.8±13.01 years; 14 males) and 27 age- and sex-matched HCs (mean age 48.67±10.16 years; 12 males) (Table 1). Shapiro-Wilk tests were conducted to evaluate the normality of the relevant continuous variables, with the results presented in Table S1. There were no significant differences between ESKD patients and HCs in terms of age (U=335.5, P=0.978) and sex (χ²=0.308, P=0.579). In the laboratory data, calcium and eGFR levels increased in ESKD patients after hemodialysis compared to pre-hemodialysis levels, whereas BUN, creatinine, and potassium levels decreased following hemodialysis (Figure 3).

Figure 3 Comparison of metrics in ESKD patients pre- and post-hemodialysis. (A) ALPS index (P=0.493), and (B) clinical data (P value range, <0.001 to 0.003). ALPS, along the perivascular space; BUN, blood urea nitrogen; eGFR, estimated glomerular filtration rate; ESKD, end-stage kidney disease.

Head motion analysis revealed no significant differences in MAID between pre-hemodialysis ESKD patients (MAID =3.64±1.07 mm), post-hemodialysis ESKD patients (MAID =3.60±1.03 mm), and HCs (MAID =3.30±0.83 mm) (ANOVA, P=0.351) (Table 1). Similarly, no significant difference was observed between pre- and post-hemodialysis scans in ESKD patients (paired t-test, P=0.761). Given the lack of significant differences, we did not include motion as a nuisance regressor in the statistical analysis.

Comparison of ALPS index

The DTI-ALPS values for the participant groups are summarized in Table 2 and Figure 4. The ALPS index was significantly lower in pre-hemodialysis ESKD patients compared to HCs (1.379 vs. 1.469, P=0.023). There was no significant difference in the ALPS index between pre- and post-hemodialysis in ESKD patients within a single session (1.379 vs. 1.387, P=0.493). Additionally, diffusivity along the x-axis and z-axis in the association fibers, as well as along the y-axis in the projection fibers, which are not considered to reflect glymphatic function based on the DTI-ALPS method (8), also showed significant differences between pre-hemodialysis ESKD patients and HCs (P<0.05).

Figure 4 Comparison of the ALPS index between ESKD patients and HCs. The ALPS index is significantly lower in ESKD patients compared to HCs (1.379 vs. 1.469, P=0.023). There is no significant difference in the ALPS index between pre- and post-hemodialysis measurements within a single hemodialysis session in ESKD patients (1.379 vs. 1.387, P=0.493). ALPS, along the perivascular space; ESKD, end-stage kidney disease; HCs, healthy controls.

After excluding 13 ESKD patients with comorbidities, the remaining subgroup (n=12) showed no significant difference in ALPS index compared to HCs (1.410 vs. 1.469, P=0.1297). Similarly, within this subgroup, no significant change was observed between pre- and post-hemodialysis measurements (1.410 vs. 1.417, P=0.5731) (Figure S1).

Correlation between ALPS index and clinical characteristics

In pre-hemodialysis ESKD patients, a significant positive correlation was observed between the ALPS index and prealbumin levels after FDR correction (r=0.663, uncorrected P=0.002, corrected P=0.036, FDR-corrected), as illustrated in Figure 5. No significant correlations were observed between the ALPS index and other laboratory parameters.

Figure 5 Correlation analysis. A significant positive correlation was found between the ALPS index and prealbumin levels in pre-hemodialysis ESKD patients (r=0.663, uncorrected P=0.002, corrected P=0.036, FDR-corrected). ALPS, along the perivascular space; ESKD, end-stage kidney disease; FDR, false discovery rate.

Exploratory analysis showed no significant correlations between pre-hemodialysis ALPS index and dialysis duration (months) (r=−0.180, P=0.390), age (r=−0.370, P=0.071), or cumulative number of hemodialysis sessions (r=−0.147, P=0.484). Similarly, the change in ALPS index (post- minus pre-hemodialysis) showed no significant associations with dialysis duration (months) (r=0.022, P=0.919), age (r=−0.185, P=0.387), or cumulative number of hemodialysis sessions (r=0.026, P=0.904).

Discussion

This study explored glymphatic system alterations in patients with ESKD and examined the potential impact of hemodialysis using the DTI-ALPS method. Our findings showed that ESKD patients exhibited lower ALPS indices compared to HCs, suggesting altered perivascular diffusivity. However, no significant change in the ALPS index was observed following a single hemodialysis session. Although these results may reflect impaired interstitial fluid dynamics in ESKD, the ALPS index remains an indirect imaging marker and does not directly quantify glymphatic clearance. Therefore, interpretations regarding glymphatic dysfunction should be made cautiously and considered within the broader context of cerebrovascular and systemic influences.

The post-hemodialysis changes observed in the laboratory results of this study, including increases in calcium and eGFR, as well as reductions in BUN, creatinine, and potassium, align with the expected biochemical responses to hemodialysis. These changes highlight the procedure’s effectiveness in waste clearance and electrolyte balance, key functions for maintaining systemic homeostasis in ESKD patients. However, although these biochemical markers improve with dialysis, they primarily reflect systemic rather than central nervous system (CNS) clearance. This distinction may explain the lack of improvement in ALPS index observed in our study. Hemodialysis effectively clears small, water-soluble molecules from the blood but is less efficient at removing larger, protein-bound uremic toxins that can cross the blood-brain barrier and accumulate in the brain (22). These retained toxins may continue to impair the glymphatic system by promoting neuroinflammation and oxidative stress, which can hinder interstitial fluid flow and reduce perivascular clearance (23). This distinction underscores the need for CNS-specific markers to evaluate dialysis efficacy fully and suggests that modified or adjunctive approaches could benefit CNS clearance in ESKD patients.

The reduced ALPS index in ESKD patients may indicate glymphatic system dysfunction, suggesting that CKD could profoundly impact brain health. In ESKD, the accumulation of uremic toxins and chronic inflammation may impair glymphatic clearance by crossing the blood-brain barrier, disrupting cerebral circulation, and limiting interstitial fluid movement through perivascular pathways (24). This dysfunction in brain waste clearance could lead to the buildup of neurotoxic proteins such as amyloid-beta, which are normally cleared by the glymphatic system and may partly explain the higher prevalence of cognitive decline in ESKD patients (25). This study confirmed that a single hemodialysis session does not enhance the ALPS index, potentially because hemodialysis cannot effectively remove certain toxins, such as middle molecules and protein-bound solutes, that contribute to chronic inflammation and oxidative stress, further damaging glymphatic function (26). Given that impaired glymphatic function has been associated with neurodegenerative conditions such as Alzheimer’s disease (27), these findings emphasize the potential role of glymphatic impairment as a neurological risk pathway in kidney disease. The lack of improvement in the ALPS index highlights the need for additional therapeutic strategies that target neuroinflammation and support glymphatic activity. Adjustments to hemodialysis protocols or complementary treatments may be critical for mitigating cognitive risks in this population. Further research is essential to uncover the mechanisms linking renal dysfunction to brain health and to develop integrated therapeutic strategies for ESKD patients.

The correlations between the ALPS index and clinical indicators enhance our understanding of the relationship between kidney function and glymphatic activity. Prealbumin, an important biomarker of nutritional status, is critical for waste clearance and neuroprotection against toxicity (28). In CKD and ESKD patients, prealbumin levels are commonly affected by malnutrition and chronic low-grade inflammation. The positive correlation between prealbumin and the ALPS index suggests that nutritional status may influence brain waste clearance mechanisms, indicating that better-nourished patients may exhibit more effective clearance capabilities. Low prealbumin levels often indicate malnutrition, which is prevalent in these populations and crucial for disease management and prognosis. Additionally, inflammation can suppress prealbumin production, meaning that low levels may reflect both nutritional deficiencies and an inflammatory state. This interplay between chronic inflammation and malnutrition, referred to as “cachexia syndrome”, can increase the risk of disease progression in CKD patients (29). Exploratory analyses also revealed no significant relationships between ALPS index or its change and dialysis duration, age, or cumulative number of hemodialysis sessions, suggesting that these factors may not be primary predictors of glymphatic function in this context; however, these null findings may reflect the small sample size and warrant further investigation with larger cohorts.

To further assess the influence of comorbidities, our sensitivity analysis excluding ESKD patients with comorbidities demonstrated that the difference in ALPS indices between ESKD patients and HCs was no longer significant. This suggests that cardiovascular and systemic comorbidities may partly contribute to impaired cerebral waste clearance in ESKD. Additionally, the reduced sample size after exclusion may have limited statistical power. Therefore, the ALPS index observed in ESKD patients should be interpreted as potentially reflecting the combined effects of kidney failure and coexisting comorbid conditions. Future studies with larger, comorbidity-stratified cohorts are warranted to better delineate these relationships.

This study has several limitations. First, the relatively small sample size and the use of a single time point for HCs may limit the generalizability of our findings and prevent the detection of inter-individual variability over time. Second, the timing of MRI acquisition relative to hemodialysis sessions (2–24 hours pre-hemodialysis and 24–48 hours post-hemodialysis) was constrained by clinical and logistical factors. This variability may have obscured transient dialysis-related changes in glymphatic activity. Third, we did not assess cognitive performance, which limits the interpretation of clinical relevance regarding neurological outcomes. Additionally, the ALPS index is an indirect measure of glymphatic activity based on perivascular water diffusivity and does not capture the full complexity of glymphatic clearance (30-32). Moreover, the use of spherical ROIs may have introduced partial volume effects, affecting measurement reliability. Ulloa et al. recommended using squared or cubic ROIs to achieve more consistent and reliable results (33). Future studies should adopt larger, stratified samples, tighter imaging schedules, and multimodal neuroimaging approaches (including direct functional assessments) to better elucidate the brain-kidney axis and its implications for cognitive health in ESKD.

Conclusions

This study found that patients with ESKD exhibited lower ALPS indices compared to HCs, suggesting potential alterations in perivascular diffusivity. However, this difference was no longer statistically significant after excluding patients with cardiovascular and systemic comorbidities, indicating that such conditions may also contribute to the observed changes. No significant change in the ALPS index was observed after a single hemodialysis session under the current scanning schedule; further studies with optimized scan timing are needed to clarify acute dialysis effects. Importantly, the ALPS index is an indirect imaging marker and does not directly reflect glymphatic clearance function. Therefore, our findings should be interpreted with caution. Future studies using larger, comorbidity-stratified cohorts and multimodal imaging approaches are needed to better understand the relationship between kidney dysfunction, systemic comorbidities, and brain fluid dynamics.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-74/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2025-74/dss

Funding: This study was supported by the Guizhou Provincial High-level Innovative Talent Training Program (QianKeHe Platform Talent-GCC [2023] 083, to X.Z.) and the National Natural Science Foundation of China (Grants Nos. 82460344 and 82060314, to X.Z.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2025-74/coif). X.Z. reports that this study was supported by the Guizhou Provincial High-level Innovative Talent Training Program (QianKeHe Platform Talent-GCC [2023] 083) and the National Natural Science Foundation of China (Grants Nos. 82460344 and 82060314). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendment and was approved by the Ethics Committee of Tongren People’s Hospital (No. 202304). Informed consent was provided by all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)