Associations between altered morphometric inverse divergence networks, choroid plexus volume, and perivascular space network in patients with Parkinson disease: a cross-sectional study

Introduction

Parkinson disease (PD) is a progressive neurodegenerative disorder characterized by morphological abnormalities in certain brain regions, particularly within structural connectomes (1-4). Structural alterations in gray-matter volume, cortical surface area, and cortical thickness have been observed in regions such as the frontal cortex, parietal lobe, temporal gyrus, and occipital cortex in patients with PD (2,5,6).

The structural connectome of the brain has been investigated via structural covariance networks (SCNs) and morphometric similarity networks (MSNs) (7,8). However, SCNs yield only a single group-level network, limiting individual-level applicability and leading to potentially controversial biological interpretations (9). Meanwhile, MSNs reduce vertex-level morphological information to a single summary statistic and assume uniform feature variability across cortical regions (10).

Based on divergence across multiple morphometric features measured at the vertex level, a novel method termed morphometric inverse divergence (MIND) has been introduced to assess structural similarity networks (11). Compared with MSNs, MIND networks demonstrate higher stability, better correspondence with cortical cytoarchitecture, stronger correlations with tractography-derived axonal connectivity measures, and greater heritability (11). Higher MIND similarity indicates that two regions share more similar structural profiles, which may reflect coordinated development, shared cytoarchitectonic properties, or synchronized remodeling processes across the brain. Conversely, lower MIND similarity reflects greater divergence in morphometric features between regions, which may indicate region-specific structural alterations, network disintegration, or asynchronous neurodegenerative processes.

The glymphatic system, a fluid transport network within the central nervous system, facilitates dynamic exchange between interstitial fluid (ISF) and cerebrospinal fluid (CSF) (12,13). This process occurs primarily through the perivascular spaces (PVS) surrounding the subarachnoid space and the small blood vessels that traverse the brain parenchyma (14). With advances in high-resolution magnetic resonance imaging (MRI) and automated choroid plexus (CP) volume segmentation techniques, CP volume has increasingly been recognized as a potential imaging marker for intracranial inflammatory processes, metabolic clearance, and CSF hydrodynamics and has thus gradually been incorporated into the research on PD (15,16).

PD has been associated with CP volume and PVS network parameters (12,16-18), and some studies have reported differences in MIND networks between patients with PD and healthy controls (HCs) (19). However, it remains unknown whether MIND network changes in patients with PD exhibit spatial associations with CP volume and PVS network parameters. This study aimed to integrate multimodal analyses to investigate the relationships among the MIND network, CP volume, and the PVS network in patients with PD, thereby providing a more systematic understanding of potential mechanisms linking MIND network alterations with CP volume and PVS network changes.

We hypothesized that alterations in the MIND network in PD are associated with CP volume and PVS network parameters. This study may facilitate the identification of novel structural imaging biomarkers and intervention targets for PD. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0242/rc).

Methods

Participants

This study included 112 patients with PD who underwent levodopa challenge testing and 83 age- and sex-matched HCs recruited from the hospital between March 2019 and November 2023. Patients with PD aged 45–85 years with a confirmed diagnosis according to the UK Parkinson’s Disease Society Brain Bank criteria were included in the analysis. Conversely, patients who had (I) atypical or secondary parkinsonism (n=5; three with vascular parkinsonism and two with drug-induced parkinsonism) and (II) poor-quality T1-weighted sagittal magnetization-prepared rapid gradient-echo (MPRAGE) images (n=8) were excluded. Among HCs, 1 individual was excluded due to poor image quality, leaving 82 HCs for analysis. The final dataset comprised 99 patients with PD and 82 HCs. All participants underwent MRI examinations, and baseline demographic data, including age, sex, and education level, were collected (Table 1). A flowchart illustrating the study process from the initial screening to the final analysis is presented in Figure 1. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committees of Shenzhen Second People’s Hospital (approval No. 2025-066-01YJ). All participants provided written informed consent.

Figure 1 Flowchart of the participant selection process. HC, healthy control; MPRAGE, magnetization-prepared rapid gradient-echo; PD, Parkinson disease.

Imaging acquisition and preprocessing

MRI data were acquired with a 3.0-T scanner (MAGNETOM Prisma, Siemens Healthineers, Erlangen, Germany) with a standard 64-channel head coil. The imaging sequence included (I) a T1-weighted sagittal MPRAGE sequence (repetition time =2,300 ms, echo time =3.55 ms, flip angle =8°, slice thickness =0.9 mm, scan time =5 min 20 s, field of view =256 mm × 256 mm, and voxel size =0.9×0.9×0.9 mm³); (II) high-resolution T2-weighted coronal double spin-echo sequence (HR-T2WI) (repetition time =4,010 ms, echo time =97 ms, flip angle =150°, slice thickness =2.0 mm, scan time =8 min 11 s, field of view =220 × 220 mm, and voxel size =0.7×0.7×2.0 mm³); and (III) diffusion tensor imaging (DTI) [b=0 and 1,000 s/mm², repetition time =8,000, echo time 64 ms, field of view =224 mm × 224 mm, flip angle =90°, slice thickness =2 mm, scan time =4 min 24 s, voxel size =2.0×2.0×2.0 mm³, and magnetic resonance parameter group (MPG) =20 directions].

CP volume

The CP volume in the lateral ventricles was automatically segmented on T1-weighted images with a Gaussian mixture model implemented in FreeSurfer v. 7.4.1 software (http://surfer.nmr.mgh.harvard.edu) (Figure 2A) (20). All automatic segmentations were visually inspected and approved by a neuroradiology expert, with manual correction applied when necessary. To account for individual differences in head size, regional CP volume was recorded as the ratio of the regional volume to the total intracranial volume (ICV) (ratio of ICV×10³) (15).

Figure 2 Schematic overview of MRI-derived indices of CP volume and the PVS network. (A) Gaussian mixture model-based CP segmentation. CP volumes within the lateral ventricles were automatically segmented on T1-weighted MR images via a Gaussian mixture model. (B) Generation of FW volume fraction maps via DIPY by fitting single-shell FW-estimated and FW-corrected DTI models. Average FW fractions were calculated for WM, with PVS regions being excluded for each participant. (C) Computation of PVS volume as the sum of the individual volumes identified in the BG and WM. To account for individual differences in intracranial volume, the PVSVF was calculated as the PVS volume divided by the intracranial volume. (D) Workflow for the calculation of DTI-ALPS indices. Diffusion data were processed with FSL for motion and eddy-current correction, which was followed by DTIFIT and registration to the standard space. FA and directional diffusivity maps (Dx, Dy, and Dz) were co-registered to the FA template. ROIs encompassing bilateral projection and association fibers were extracted based on atlas labels to minimize manual delineation-related bias. Directional diffusivity values were measured for each ROI, including Dxproj and Dxassoc for the x-axis components of the projection and association fibers and Dyproj and Dzassoc for the y- and z-axis components. Directional diffusivity values were measured for each ROI (Dxxproj, Dxxassoc, Dyyproj, and Dzzassoc), and ALPS indices were calculated as the mean (Dxxproj, Dxxassoc)/mean(Dyyproj, Dzzassoc). 3D, three-dimensional; ALPS, analysis along the perivascular space; BG, basal ganglia; CP, choroid plexus; DIPY, Diffusion Imaging package in Python; DTI, diffusion tensor imaging; DTI-ALPS, diffusion tensor imaging-analysis along the perivascular spaces; DTIFIT, diffusion tensor imaging fitting; DWI, diffusion weighted imaging; Dx, diffusivity along the x-axis; Dy, diffusivity along the y-axis; Dz, diffusivity along the z-axis; FA, fractional anisotropy; FMRIB, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain; FSL, FMRIB Software Library; FW, free water; HR-T2w, high-resolution T2-weighted; MR, magnetic resonance; MRI, magnetic resonance imaging; PVS, perivascular space; PVSVF, perivascular space network volume fraction; ROI, region of interest; T1w, T1-weighted; WM, white matter.

Free water (FW) fraction in white matter (WM)

A single-shell FW estimation model was applied to an FW-corrected DTI dataset via the DIPY package (version 1.4.0) in Python (Python Software Foundation, Wilmington, DE, USA) to generate FW maps. Notably, periventricular WM was explicitly excluded from the analysis to mitigate severe partial volume effects originating from the adjacent CSF. Given that single-shell FW algorithms may incompletely eliminate CSF contamination at the direct tissue-fluid interface, this conservative approach ensured that the derived FW metrics accurately reflected genuine tissue microstructural alterations. Subsequently, the mean FW fraction in the WM (excluding these periventricular regions) was calculated from the maps (Figure 2B).

PVS volume fraction (PVSVF) in the WM

We employed an automated quantification method developed by our team based on three-dimensional (3D) T1- and high-resolution T2-weighted images to generate PVS maps (21). The total volume of identified PVS in the basal ganglia (BG) and WM was calculated as the sum of the individual PVS volumes. To account for interindividual differences in ICV, the PVSVF was calculated as the ratio of PVS volume to ICV (Figure 2C).

DTI-ALPS procedure and measurement

The DTI-ALPS index was calculated according to a previously established method (Figure 2D) (22). First, DTI data were processed with motion correction, eddy-current correction, DTI fitting, and registration to standard space. Fractional anisotropy (FA) and diffusivity maps along the x-axis (Dx), y-axis (Dy), and z-axis (Dz) were then co-registered to FA templates provided by the Functional MRI of the Brain (FMRIB) Software Library (FSL). Regions of interest (ROIs) containing bilateral projection and association fibers were extracted based on atlas labels to minimize manual delineation-related bias. Within each ROI, diffusivities along the x-axis in projection (Dxxproj) and association (Dxxassoc) fibers and along the y-axis (Dyyproj) and z-axis (Dzzassoc) were measured. The ALPS index was subsequently calculated as follows:

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

MIND network construction

Cortical surfaces were parcellated into 148 spatially contiguous regions (74 per hemisphere) based on the Destrieux atlas (23). A backtracking algorithm was applied to ensure that each region was approximately equal in size (approximately 500 mm²). The atlas was individually mapped to each participant’s cortical surface for customized segmentation (24). Vertex-wise morphometric features were extracted for each cortical region, including the cortical thickness, cortical volume, surface area, mean curvature, pial surface area, and pial surface curvature (24). Interregional similarity was quantified via the transformed Kullback-Leibler (KL) divergence between the multidimensional feature distributions of each region pair, yielding similarity values between 0 and 1. This process generated a 148×148 MIND similarity matrix for each participant. The detailed computational steps are illustrated in Figure 3.

Figure 3 Construction of MIND networks. Cortices were divided into 148 regions according to the Destrieux atlas. Morphometric features—including cortical thickness, volume, area, curvature, sulc, area.pial, and curv.pial—were extracted from T1w images to construct MIND networks. area.pial, pial surface area; curv, curvature; curv.pial, pial surface curvature; HC, healthy control; KL, Kullback-Leibler; MIND, morphometric inverse divergence; PD, Parkinson disease; sulc, sulcus; T1, longitudinal relaxation time; T1w, T1-weighted.

Case-control analysis of regional MIND

To characterize region-wise alterations in whole-brain morphometric similarity, regional MIND strength was calculated for each cortical region. For each node, MIND strength was defined as the average MIND similarity between that region and all other cortical regions in the network. This metric, corresponding to weighted nodal strength in graph theory (25), reflects the overall degree of morphometric connectivity with the rest of the brain.

Statistical analysis

Continuous variables (expressed as mean ± standard deviation) and categorical variables (expressed as counts) were compared via two-sample t-tests, Mann-Whitney U tests (guided by Kolmogorov-Smirnov normality tests), or Chi-squared tests. Regional MIND differences (n=148) between the PD and HC groups were analyzed via two-sample t-tests. The associations between CP volume, PVS network parameters, and MIND values in regions exhibiting significant abnormalities were examined according to Pearson correlation coefficients. To control for multiple comparisons, false-discovery rate (FDR) correction (P<0.05) was applied to both the regional MIND group comparisons and all correlational analyses. Analyses were performed with SPSS v. 26.0 (IBM Corp., Armonk, NY, USA) and MATLAB R2024a (MathWorks, Natick, MA, USA).

Results

Clinical and demographic characteristics

After 13 patients with PD and 1 HC were excluded due to poor image quality, the final analysis included 99 patients with PD (age range, 40–80 years) and 82 HCs (age range, 45–75 years). Table 1 summarizes the demographic characteristics and PVS-related measures of the study participants. No significant differences were observed between groups in terms of sex (Chi-squared test; χ²=0.71; P=0.40), age (two-sample t-test; t=0.46; P=0.65), or education (two-sample t-test; t=0.49; P=0.75).

Regional MIND differences in PD

After FDR correction (P<0.05), MIND values differed significantly in 19 cortical regions between patients with PD and HCs (Figure 4). Patients with PD showed decreased regional MIND values in five regions (Table 1): the left middle occipital gyrus, left occipital pole, right transverse frontopolar gyrus, right middle occipital gyrus, and right lunate sulcus. Increased regional MIND values were observed in 14 regions, including the bilateral central sulcus, bilateral subcallosal gyrus, bilateral medial orbital gyrus, left parahippocampal gyrus, left planum polare of the superior temporal gyrus, left medial part of the inferior temporal gyrus, left lingual gyrus, left inferior part of the precentral sulcus, right long insular gyrus, right short insular gyrus, right marginal part of the cingulate sulcus, and right inferior parietal sulcus (Table 1).

Figure 4 Case-control differences in regional morphometric similarity. The raincloud plots illustrate the distribution of regional MIND values in brain regions showing significant differences between HCs and patients with PD. Compared with HCs, patients with PD showed significantly lower MIND values in the left middle occipital gyrus (A), left occipital pole (E), right transverse frontopolar gyrus (J), right middle occipital gyrus (M), and right lunate sulcus (Q). Conversely, significantly higher MIND values were observed in the bilateral central sulcus (F,O), bilateral subcallosal gyrus (C,N), bilateral medial orbital gyrus (H,R), left parahippocampal gyrus (B), left planum polare of the superior temporal gyrus (D), left medial part of the inferior temporal gyrus (G), left lingual gyrus (G), left inferior part of the precentral sulcus (I), right long insular gyrus (K), right short insular gyrus (L), right marginal part of the cingulate sulcus (P), and right inferior parietal sulcus (S). All results remained significant after FDR correction. Exact P values are shown in the figure. FDR, false-discovery rate; HC, healthy control; MIND, morphometric inverse divergence; PD, Parkinson disease.

Correlations of regional MIND values with CP volume and PVS network

As an exploratory analysis, associations between regional MIND values in patients with PD and CP volume and PVS network parameters were examined (Figure 5). After FDR correction, MIND values in five cortical regions (left superior frontal gyrus, left middle temporal gyrus, left occipital pole, left central sulcus, and right paracentral lobule) were positively correlated with CP volume (R=0.29; P<0.05). MIND values in 72 cortical regions were positively correlated with FW fraction (R=0.28–0.60; P<0.05), including 10 of the 19 regions that differed between patients with PD and HCs. Additionally, MIND values in the left planum polare of the superior temporal gyrus, left middle temporal gyrus, left central sulcus, and right central sulcus were negatively correlated with the DTI-ALPS index (R=−0.36 to −0.37; P<0.05). Before FDR correction, regional MIND values in nine cortical regions were correlated with PVSVF, BG-PVSVF, and WM-PVSVF; however, none of these correlations remained significant after correction (P>0.05).

Figure 5 Heatmaps showing correlations of MIND values with the left (A) and right (B) hemispheric cortical regions, CP volume, and PVS network in PD. BG-PVSVF, basal ganglia-perivascular space volume fraction; CP, choroid plexus; DTI-ALPS, diffusion tensor imaging-analysis along the perivascular spaces; FW, free water; MIND, morphometric inverse divergence; PD, Parkinson disease; PVS, perivascular space; PVSVF, perivascular space volume fraction; WM-PVSVF, white matter-perivascular space volume fraction.

Discussion

This study employed the MIND network to investigate regional cortical differences between patients with PD and HCs and to examine the associations of the MIND network with both CP volume and PVS network in PD. Unlike previous research that focused primarily on morphological alterations in isolated brain regions (26,27), our study revealed whole-brain reorganization of cortical structural similarity in PD, providing a network-level perspective on disease-related cortical changes. Given the absence of matched HC comparisons for certain imaging metrics, our findings should be interpreted as reflecting intragroup variability and associations within the PD cohort rather than definitive disease-specific alterations.

In our study, MIND values were predominantly decreased in the frontal and occipital cortices and increased in the cingulate and insular cortices. This heterogeneous pattern suggests that PD is not characterized by uniform cortical degeneration but rather by imbalanced remodeling of regional morphometric similarity and network integration.

From a biological perspective, reduced MIND in a given region reflects weakened morphometric similarity between that region and other cortical areas, potentially indicating neuronal loss, cortical atrophy, or asynchronous degenerative processes across regions (11,28). As a key region for executive function and emotional regulation, the frontal cortex may exhibit decreased MIND values in patients with early-stage PD due to structural network disintegration, which is consistent with previous studies reporting cognitive and executive impairments in PD (29-31). By contrast, increased MIND values in the cingulate and insular cortices indicate enhanced morphometric similarity between these regions and other brain areas (11,28). As integral components of the emotion-regulation and salience networks, elevated MIND values in these regions may reflect compensatory or reorganizational changes. This pattern suggests that, during disease progression, some core network nodes may increase structural coordination with other regions to maintain overall functional stability. However, such increased structural similarity may also reflect network over-synchronization, which could potentially reduce the flexibility of information processing.

In patients with PD, MIND values were positively associated with CP volume in multiple cortical regions, including the left superior frontal gyrus, left middle temporal gyrus, left occipital pole, left central sulcus, and right paracentral lobule. Associations with these regions, primarily located in the frontal, temporal, and occipital lobes, as well as the sensorimotor cortex, suggest that changes in CP volume may be related to multisystem structural network remodeling in PD.

Regarding function and anatomy, the superior frontal gyrus and middle temporal gyrus are important cortical regions involved in higher cognition, language processing, and emotion regulation (32,33). The observed association between MIND values in these regions and CP volume suggests that structural network abnormalities may be associated with altered CSF production and regulation of the brain microenvironment. Previous studies have indicated that increases in CP volume are characterized primarily by expanded interstitial tissue and a relative reduction in vascular structures (34,35). Such changes may accompany MIND network remodeling across cortical and subcortical regions, with macroscopic connectivity patterns potentially adapting in response to altered CSF dynamics and inflammatory states. Integrating CP volume with MIND network analysis may therefore provide more sensitive biomarkers for early diagnosis and intervention for patients with PD. Due to technical and anatomical constraints, restricting CP segmentation to lateral ventricles yields a partial measurement that may underestimate total volume and its associations with MIND network.

FW is considered an imaging marker of extracellular FW in brain tissue, and higher FW is often associated with neuroinflammation, cellular injury, axonal degeneration, and reduced microstructural integrity (36-38). In our study, MIND values in nearly half of the cortical regions were positively correlated with FW, suggesting that regions with elevated FW exhibit greater morphometric similarity to other cortical areas. However, this increased structural similarity does not necessarily indicate improved network function, and it may instead reflect a pathological state in which neuronal or glial injury produces a more homogeneous tissue architecture. Consequently, morphometric distinctions between regions may be diminished, yielding abnormally elevated MIND values. From a biological and clinical perspective, these findings suggest that microstructural alterations related to neuroinflammation and changes in brain ISF dynamics may be associated with large-scale cortical reorganization in PD. While such alterations have been hypothesized to partly relate to glymphatic processes, FW is a nonspecific DTI-derived metric influenced by multiple biological factors (39,40). Therefore, the observed coupling between microstructural changes and macrostructural network alterations should be interpreted as reflecting complex microenvironmental disturbances rather than glymphatic dysfunction. However, the observed correlations were modest in magnitude (R=0.28–0.60). This is not unexpected in multimodal neuroimaging studies, as FW is influenced by a range of interacting biological processes rather than by a single pathological pathway. Given this multifactorial nature, along with sample heterogeneity and the lack of covariate adjustments, these associations should be interpreted with appropriate caution.

Significant negative associations were observed between MIND values and the DTI-ALPS index in multiple cortical regions, including the left superior and middle temporal gyri and the bilateral central sulci, encompassing cognition-related areas and primary motor cortices. Methodologically, the DTI-ALPS is a DTI-derived index representing the ratio of water diffusivity components orthogonal and parallel to the deep medullary veins within the WM fibers near the lateral ventricular bodies (41,42). Although previously proposed as an indirect marker of glymphatic function, recent studies have raised valid concerns regarding whether this index reflects glymphatic clearance (36-38,41,42). Crucially, because PD affects WM independently, the DTI-ALPS alterations observed in our study might be heavily confounded by these primary periventricular WM changes. Therefore, rather than definitively indicating glymphatic dysfunction, the association between the DTI-ALPS index and MIND values can be more cautiously interpreted as reflecting altered local WM diffusivity and microenvironmental disruption. These localized microstructural and fluid dynamic changes may promote abnormal cortical remodeling, leading to increased morphometric similarity (potentially reflecting abnormal network synchronization or compensatory reorganization) observed in patients with PD. Overall, while this association provides insight into combined macro- and microstructural pathological mechanisms in PD, conclusions regarding specific glymphatic impairment cannot be considered definitive.

Analysis of the PVS volume-related indices (PVSVF, BG-PVSVF, and WM-PVSVF) indicated no significant correlations with the MIND values after FDR correction. This finding suggests that abnormalities in structural networks in PD and glymphatic-related characteristics derived from PVS volume may not exhibit a linear relationship. Alterations in PVS may follow a distinct temporal trajectory and exhibit spatial decoupling from changes in the cortical morphological network. Previous studies have reported that DTI-ALPS and PVS load indices correlate more consistently with PVS count than with BG PVS volume, suggesting that PVS number may represent a more sensitive marker of glymphatic function (16). By contrast, PVS volume is more susceptible to nonspecific influences, including brain atrophy, vascular status, and imaging resolution (43,44). Consequently, the reliance on PVS volume-based indices in our study might have limited the sensitivity in detecting glymphatic abnormalities.

The influence of PVS on brain microstructure likely occurs through changes in the local microenvironment and WM diffusion properties rather than through the direct modification of cortical morphological networks. Accordingly, the absence of a direct association between PVS volume and MIND does not preclude a role for PVS in PD pathology but instead suggests that PVS and MIND may be indirectly linked through mediating factors.

Limitation

This study involved several limitations that should be addressed. First, the retrospective, cross-sectional design precludes causal inference, dynamic tracking across disease stages, and subgroup analyses of specific PD phenotypes. Second, analyzing patients with PD as a single cohort—without stratifying by disease duration, incorporating clinical severity metrics (e.g., Unified Parkinson’s Disease Rating Scale and Hoehn and Yahr stage), or adjusting for key covariates (age, sex, and disease duration)—limits direct clinical interpretability and introduces potential sample heterogeneity. Consequently, our findings should be interpretated with caution and validated in large, multicenter longitudinal studies. Third, although DTI-ALPS, PVSVF, and FW-WM have been widely proposed as indirect glymphatic markers, recent studies question their capacity to accurately reflect true clearance. As static metrics reflect structural alterations rather than dynamic fluid transport, their physiological validity remains to be further confirmed against the gold standard (intrathecal gadolinium administration) (41,42). Finally, we did not examine the associations with region-specific gene expression, and thus future studies integrating transcriptomic data are needed to elucidate the genetic mechanisms underlying MIND network alterations.

Conclusions

This study identified distinct patterns in the MIND network in patients with PD and identified significant associations between MIND values, CP volume, and PVS-related imaging markers. These findings advance the understanding of macrostructural brain alterations in PD and help delineate the distinct relationships between cortical structural networks, CP volume, and glymphatic-related measures. Collectively, the results provide a network-level framework for interpreting structural brain alterations in patients with PD and may inform future research on imaging biomarkers and the identification of therapeutic targets.

Acknowledgments

We would like to thank the patients and volunteers who participated in this study and all researchers who assisted in data collection and management. We would like to thank Editage (www.editage.cn) for English language editing.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0242/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0242/dss

Funding: This study was supported by the National Natural Science Foundation of China (No. 82171913 and No. 82572199) and Shenzhen Medical Research Fund (No. C2501009).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0242/coif). All authors report that this study was supported by the National Natural Science Foundation of China (No. 82171913 and No. 82572199) and Shenzhen Medical Research Fund (No. C2501009). The authors have no other 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. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committees of Shenzhen Second People’s Hospital (approval No. 2025-066-01YJ) and informed consent was obtained from 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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