Enhanced hippocampal-cortical functional connectivity following post-stroke cognitive impairment: a resting-state functional magnetic resonance imaging study

Introduction

Acute ischemic stroke (AIS) is a cerebrovascular disease caused by an acute interruption of cerebral blood flow due to arterial occlusion, leading to ischemia and infarction of brain tissue (1). AIS represents a major global public health challenge and is the most prevalent stroke subtype, accounting for over 70% of all stroke cases (2). Characterized by high rates of mortality and long-term disability, it imposes a substantial burden on individuals, families, and healthcare systems (3). The majority of AIS survivors experience various functional impairments, including motor deficits, mood disorders, and language disturbances. Notably, approximately two-thirds of these patients develop cognitive impairment (CI) within three months post-stroke (4). Post-stroke cognitive impairment (PSCI) is a clinical syndrome involving deficits across multiple cognitive domains, such as memory, attention, language, executive function, and visuospatial abilities (5). Brain injury in PSCI extends beyond the focal lesion site, often leading to widespread disruption of large-scale neural networks and consequent impairments in brain function. The hippocampus, a critical brain region involved in learning, memory, and emotional regulation, has long been a focus of cognitive neuroscience research. Accumulating evidence indicates that PSCI patients exhibit hippocampal volume atrophy, as well as abnormalities in hippocampal-related neural circuits and functional connectivity (FC) (6,7). Although existing studies suggest that structural and functional alterations in the hippocampus constitute a key neurobiological substrate for PSCI, the specific patterns of aberrant FC between the hippocampus and other brain regions with their relationship to cognitive deficits remain poorly understood.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive neuroimaging technique that enables the assessment of spontaneous brain activity through blood oxygen level-dependent (BOLD) signals (8). It provides valuable insights into neural function by analyzing regional measures such as amplitude of low-frequency fluctuations (ALFF), fractional ALFF, and regional homogeneity, while also allowing the evaluation of FC between brain regions based on temporal correlations of BOLD signals (9,10). FC quantifies the synchronized activity between spatially separated brain areas, reflecting their functional integration within neural networks (11). Seed-based correlation analysis is a widely used approach for mapping whole-brain FC, in which the time series of a predefined seed region—such as the hippocampus—is extracted and correlated with the time series of all other voxels in the brain to identify functionally connected networks (12). FC analysis based on rs-fMRI has become an important means to understand the neurocognitive mechanism of PSCI. Zhang et al. reported abnormal cerebellar activity and disrupted FC between the cerebellum and limbic system in patients with PSCI, highlighting that dysfunction extends beyond canonical cortical cognitive networks to include subcortical-cortical circuits (13). Zhao et al. investigated altered FC of hippocampus sub-regions in patients with post-stroke dementia, emphasizing connectivity differences among hippocampus sub-regions rather than the whole-brain connectivity pattern of the hippocampus during the earlier stages of cognitive decline (14). Similarly, Jung et al. demonstrated disrupted hippocampus FC in patients with CI after AIS, but their study primarily described general connectivity alterations across large-scale networks and did not specifically identify distinct cortical target regions that may participate in compensatory cognitive processes (15). Most existing studies have revealed damaging FC abnormalities in subcortical structures such as the hippocampus and cerebellum of PSCI in chronic stage and their circuits, or have only described universal connectivity changes in brain networks. None of them have paid attention to abnormal FC in the early stage of disease onset or identified the key cortical target areas mediating cognitive compensation. Compared with prior studies, this study provides several novel contributions. First, we identify a specific hippocampal-prefrontal connectivity pathway, particularly involving the right frontal inferior opercular. Second, we demonstrate consistent associations between FC strength and both Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) scores, enhancing the robustness and clinical interpretability of the findings. Third, we focus on the early post-stroke stage, capturing functional reorganization during a critical window for neuroplasticity. These findings extend prior work by Zhang et al. and Jung et al., which primarily emphasized broader network disruptions without identifying specific cortical targets or early-stage compensatory mechanisms.

Therefore, the present study employed a seed-based rs-fMRI approach to systematically examine the whole-brain FC status of the bilateral hippocampus with PSCI patients. Particular attention was paid to the connectivity between the hippocampus and cortical regions, which are key nodes of cognitive control networks. In addition, we further explored the associations between altered hippocampus FC and cognitive performance measured by MoCA and MMSE, aiming to provide potential neuroimaging biomarkers for PSCI and to highlight candidate neural targets for future neuromodulatory interventions. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0015/rc).

Methods

General information

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments (16). Ethical approval was granted by the Institutional Ethics Committee of The First Affiliated Hospital of Xinjiang Medical University (No. K202505-30), and all participants in this study provided informed consent. Patients with AIS who presented to the Department of Neurology and Emergency Medicine in The First Affiliated Hospital of Xinjiang Medical University between June 2025 and October 2025 were enrolled in this retrospective cross-sectional study. The inclusion criteria were as follows: (I) confirmation of AIS diagnosis according to established clinical and imaging criteria (17); (II) symptom onset within 14 days prior to admission; (III) ability to complete MRI scanning and undergo standardized neurological and cognitive assessments. We also recruited a healthy control (HC) group from the community, matching the patient group in terms of sex, age, and educational level. The inclusion criteria for the HC group were as follows: (I) MMSE score ≥27 and MoCA score ≥26 (adjusted for education when applicable); (II) ability to tolerate MRI examination. The exclusion criteria applied uniformly across all groups were as follows: (I) history of traumatic brain injury, intracerebral hemorrhage, or intracranial space-occupying lesions; (II) hemodynamic instability or critical illness; (III) presence of severe MRI artifacts that compromise image quality for diagnostic or analytical purposes; (IV) infarction involving the medial temporal lobe.

Cognitive function assessment

At 3 months post-stroke, cognitive function was assessed via telephone using the MMSE) and the MoCA (18,19). CI was defined as a MoCA score <26 or an MMSE score <27; for individuals with ≤12 years of formal education, 1 point was added to the MoCA score to account for educational bias (20,21).

Sample size calculation

Sample size estimation was conducted using G*Power (http://www.gpower.hhu.de/), based on an effect size (Cohen’s d) of 0.8, a significance level (α) of 0.005, and 80% statistical power. Given that neuroimaging studies typically require larger effect sizes and more stringent alpha thresholds to account for multiple comparisons, a total sample of 66 participants (33 per group) is necessary to achieve adequate power. To accommodate an anticipated 10% dropout rate, we needed to recruit at least 74 participants. Thus, a total of 43 AIS patients were recruited from The First Affiliated Hospital of Xinjiang Medical University and 40 HC without a history of CI were recruited from the community. A total of 75 participants who met the inclusion and exclusion criteria were enrolled in study finally, comprising 40 HC and 35 PSCI patients. The sample size was deemed appropriate for the study objectives (22).

Scanning protocol

All participants underwent head MRI scans using the Signa Architect 3.0T MR scanner (GE Healthcare, Milwaukee, WI, USA), combined with a 48-channel head coil. The scanning range was from the foramen magnum to the top of the skull. The scanning sequences and parameters were as follows: fluid-attenuated inversion recovery (FLAIR)-T2: repetition time (TR) =8,510 ms, echo time (TE) =100 ms, slice thickness =6.0 mm, slice interval =1.0 mm, field of view (FOV) =240 mm × 240 mm, acquisition time =68 s. Diffusion-weighted imaging (DWI): TR =3,085 ms, TE =82 ms, slice thickness =6.0 mm, slice interval =1.0 mm, FOV =240 mm × 240 mm, acquisition time 47 s, b value =0 and 1,000 s/mm². BOLD: TR =2,000 ms, TE =30 ms, number of slices =33, slice thickness =3.5 mm, slice interval =0.7 mm, FOV =240 mm × 240 mm, matrix 64 × 64, acquisition time =8 minutes. Three-dimensional (3D)-T1-weighted imaging (T1WI) Magentization Prepared Rapid Gradient Echo (MPRAGE): TR =2,200 ms, TE =3.4 ms, slice thickness =1.0 mm, number of slices =170, slice interval =1.0 mm, FOV =240 mm × 240 mm, matrix =256 × 256, acquisition time =5 minutes.

Image data processing

Image preprocessing was conducted using a standardized pipeline to ensure data integrity and analytical rigor. First, raw Digital Imaging and Communications in Medicine (DICOM) images were converted to Neuroimaging Informatics Technology Initiative (NIfTI) format using dcm2niix, and image quality was visually inspected using MRIcroGL to exclude datasets with severe artifacts or motion degradation. Subsequent preprocessing steps were implemented in MATLAB 2022b (MathWorks, Natick, MA, USA) with SPM12 (https://www.fil.ion.ucl.ac.uk/spm) and RESTplus xjView software (https://www.alivelearn.net/xjview/). The procedure included: (I) removal of the first 10 volumes to allow for signal stabilization; (II) slice timing correction to account for temporal offsets across slices; (III) realignment for head motion correction, with cases exceeding a displacement threshold of ±2 mm or angular rotation of ±2° excluded from further analysis; (IV) spatial normalization involving co-registration of each case’s T1-weighted structural image to their mean BOLD functional image, followed by segmentation into gray matter, white matter, and cerebrospinal fluid; the resulting gray matter tissue probability maps were then nonlinearly normalized to the Montreal Neurological Institute (MNI) standard template; these transformation parameters were applied to all motion-corrected functional volumes to yield spatially normalized functional images, which were resampled to a voxel size of 3 mm × 3 mm × 3 mm; (V) spatial smoothing with a 6 mm × 6 mm × 6 mm full-width at half-maximum Gaussian kernel; (VI) detrending to remove linear and quadratic signal drifts arising from scanner instability or physiological fluctuations; (VII) nuisance covariate regression, including signals from white matter and cerebrospinal fluid compartments, as well as 24 motion-related regressors (six rigid-body parameters and their temporal derivatives and squared terms); (VIII) temporal band-pass filtering (0.01–0.08 Hz) to retain low-frequency fluctuations with established neurophysiological relevance (23,24).

Region of interest (ROI) selection

The left and right hippocampus were defined according to the Anatomical Automatic Labeling atlas and selected as seed regions. The mean time series of all voxels within each ROI was extracted and used as the seed signal. For each seed, whole brain FC maps were generated by computing Pearson correlation coefficients between the seed time series and time series of all other brain voxels. The resulting correlation coefficients were then converted to Z-scores using Fisher’s r-to-Z transformation to improve normality for group-level statistical analysis. This procedure yielded individual 3D FC maps for subsequent analyses (25). The data processing flowchart is shown in Figure 1.

Figure 1 The data processing flowchart. 3D, three-dimensional; MPRAGE, Magentization Prepared Rapid Gradient Echo; PACS, Picture Archiving and Communication System; rs-fMRI, resting-state functional magnetic resonance imaging; T1WI, T1-weighted imaging.

Statistical analysis

Statistical analyses were performed using SPSS 25.0 (IBM Corp., Armonk, NY, USA). Normality of distribution was assessed using the Kolmogorov-Smirnov test. For normally distributed continuous variables, data are presented as mean ± standard deviation and group comparisons were conducted using independent samples t-tests. Non-normally distributed continuous variables were reported as median (interquartile range) and analyzed using the Wilcoxon rank-sum test. Categorical variables were expressed as frequency (percentage) and compared using the Chi-squared test. To evaluate differences in FC between the patient group and HC with respect to the hippocampus seed regions and whole-brain voxels, two-sample t-tests were performed within a general linear model framework, adjusting for sex, age, and years of education as covariates. Statistical significance for neuroimaging analyses was set at a voxel-level threshold of P<0.005, combined with cluster-level family-wise error correction based on Gaussian random field theory at P<0.05. Pearson correlation analyses were conducted to examine the association between significantly altered FC strengths and cognitive performance, as measured by the MoCA and MMSE scores. To account for multiple comparisons, false discovery rate (FDR) correction was applied, and correlations were considered statistically significant at FDR-corrected P<0.05.

Results

Clinical data analysis

We excluded 8 patients were due to excessive head motion artifacts (n=4), missing cognitive assessment scores (n=3), or the presence of intracranial space-occupying lesions (n=1). A total of 40 HC and 35 PSCI patients were included in the final data analysis. No significant differences were observed between the two groups in terms of age (t=−0.081, P=0.936), sex (χ²=0.670, P=0.413), or years of education (t=0.28, P=0.781), indicating good baseline comparability. Compared with the HC group, the PSCI group exhibited significantly lower MoCA (Z=0.203, P<0.001) and MMSE (Z=0.129, P<0.001) scores. The lesions of stroke mainly occurred in the area supplied by the internal carotid artery, with a nearly equal distribution on both the left and right sides. The lesions were mainly located in the basal ganglia, corona radiata, and thalamus, whereas the proportion of lesions in other areas was relatively low. Detailed results are presented in Table 1.

Resting-state fMRI FC analysis using the hippocampus as a seed region

Compared with the HC group, the PSCI group showed significantly enhanced FC between left hippocampus and following brain regions: left superior temporal pole, right frontal inferior opercular, left inferior parietal, right superior frontal gyrus (Table 2, Figure 2). Additionally, enhanced FC was observed between right hippocampus and right frontal inferior opercular as well as right supramarginal gyrus in the PSCI group (Table 2, Figure 3). A schematic diagram of FC between the hippocampus and differential brain regions is presented in Figure 4.

Figure 2 The brain regions showing significant differences in FC between the two groups, with left hippocampus serving as the seed region. (A) A three-dimensional image. (B) A slice image. FC, functional connectivity; L, left; R, right.

Figure 3 The brain regions showing significant differences in FC between the two groups, with right hippocampus serving as the seed region. (A) A three-dimensional image. (B) A slice image. FC, functional connectivity; L, left; R, right.

Figure 4 A schematic diagram of FC between the hippocampus and differential brain regions. The blue dots represent brain ROI, and the red lines represent brain regions that show an enhancement in functional brain connectivity. Frontal_Inf_Oper, frontal inferior opercular; Frontal_Sup, Superior frontal; L, left; Parietal_Inf, inferior parietal; R, right; ROI, region of interest; Temporal_Pole_Sup, superior temporal pole.

Correlation analysis

Correlation analyses revealed that enhanced FC between right hippocampus and right frontal inferior opercular was positively correlated with both MMSE scores (r=0.467, P=0.001) and MoCA scores (r=0.434, P=0.003) in the PSCI group, suggesting a potential association between this neural pathway and cognitive performance (Figure 5).

Figure 5 Correlation analysis results. (A) MMSE. (B) MoCA. Frontal_Inf_Oper, frontal inferior opercular; MMSE, Mini-Mental State Examination; MoCA, Montreal Cognitive Assessment; R, right.

Discussion

This study utilized rs-fMRI with seed-based FC analysis, integrated with cognitive scale assessments, to investigate the specific pattern of FC between the bilateral hippocampus and whole brain in with PSCI patients. We observed that AIS not only induces focal neurological deficits but also disrupts FC between deep subcortical structures and the cerebral cortex. The FC abnormalities between hippocampus and cerebral cortex in AIS patients are not uniformly distributed across the entire brain, but are primarily confined to key cortical regions associated with higher cognitive functions, thereby increasing the risk of CI.

The hippocampus is located in the medial temporal lobe and constitutes a core component of the limbic system (26,27). It plays a central role in higher-order cognitive functions, including information encoding, learning, and memory formation. Extensive neuroscientific evidence has demonstrated that the hippocampus serves not only as a critical hub for the consolidation of short-term into long-term memory but also as an essential structure involved in spatial navigation, episodic memory formation, and memory integration (28,29). Given its pivotal role in cognitive processing, the hippocampus has been widely adopted as an ROI in neuroimaging studies. Seed-based FC analysis is a well-established method used to investigate whole brain functional interactions with a predefined seed region. This approach generates a voxel-wise map reflecting the temporal correlation between the seed region and all other brain regions, thereby characterizing the FC profile of the seed across the entire brain (30). Therefore, in this study, the bilateral hippocampus was selected as the seed region to precisely examine its activity patterns and FC alterations associated with PSCI.

This study revealed a specific pattern of enhanced FC between hippocampus and frontal inferior opercular involved in language processing and executive functions such as cognitive control and working memory in PSCI patients. The enhanced FC was predominantly localized to higher-order association areas, including right frontal lobe, bilateral parietal lobes, and left temporal lobe. No statistically significant alterations were observed in the occipital cortex, a brain region critically involved in primary visual processing. This spatial specificity suggests that post-stroke functional reorganization preferentially affects networks underlying advanced cognitive functions rather than sensory-level processing (31). Specifically, both left and right hippocampus exhibited widespread enhances in FC with frontal inferior opercular, with the most pronounced enhancement observed in right frontal inferior opercular. Additionally, left hippocampus showed strengthened connectivity with left superior temporal pole, left inferior parietal, and right superior frontal, whereas right hippocampus demonstrated enhanced coupling with right supramarginal gyrus. These regions are integral components of critical large-scale brain networks, including frontoparietal control network and the default mode network (DMN). Notably, right frontal inferior opercular is implicated in language production and executive control, whereas left inferior parietal may contribute to spatial cognition—findings that align closely with the characteristic cognitive deficits in PSCI, such as impairments in language, executive function, attention, and working memory (32). These results partially converge with those reported by Wang et al. (33), who found reduced within-network connectivity in high-level cognitive systems (e.g., bilateral frontoparietal network) but increased cross-network connectivity between primary sensory networks (e.g., visual network) and higher-order cognitive networks (e.g., DMN, right frontoparietal network) in pontine stroke patients. In contrast, the present study highlights enhanced connectivity between hippocampus and specific higher-order association cortices—particularly the inferior frontal gyrus—suggesting that distinct stroke locations may drive divergent patterns of functional reorganization. Furthermore, Dacosta-Aguayo et al. (34) reported disrupted intra-network connectivity within the DMN at three months post-stroke, which correlated with deficits in semantic and phonemic fluency as well as MMSE scores, potentially reflecting heterogeneous neuroplastic processes influenced by time since stroke or differing pathophysiological mechanisms. Ding et al. (35) further underscored the pivotal role of hippocampus connectivity in PSCI, demonstrating greater enhancement in hippocampus-medial prefrontal cortex connectivity in PSCI patients compared to post-stroke non-CI individuals, with left hippocampus connectivity predicting MoCA scores at three months. The present discovery reveals that enhanced connectivity between right hippocampus and frontal inferior opercular is positively correlated with both MoCA and MMSE scores, which complements these prior results, collectively emphasizing the prognostic significance of hippocampus cortical circuit integrity. Importantly, this study extends previous work by precisely localizing this compensatory interaction to frontal inferior opercular, offering a more anatomically refined target for future mechanistic and therapeutic investigations.

Furthermore, this study demonstrates that the enhanced FC between right frontal inferior opercular and right hippocampus is positively correlated with cognitive performance as measured by the MoCA and MMSE scores. Zhao et al. similarly reported that dynamic FC within hippocampus sub-regions is associated with cognitive assessments, reinforcing the critical role of the hippocampus-frontal cortical circuit in compensating for post-stroke cognitive deficits (14). From a mechanistic perspective, hippocampal-prefrontal interactions may support cognitive recovery through oscillatory coupling, particularly in the theta frequency band. Previous studies have demonstrated that hippocampal-prefrontal theta synchronization plays a critical role in working memory, episodic memory retrieval, and cognitive control. The increased FC observed in rs-fMRI may reflect enhanced coordination between these regions at the neural level. In PSCI, strengthened hippocampal-prefrontal coupling may facilitate the integration of memory-related processing with executive control functions, thereby supporting compensatory cognitive recovery. This provides a biologically plausible link between large-scale FC changes and underlying neural dynamics (36). Collectively, this pattern of increased connectivity suggests a compensatory mechanism whereby the brain strengthens functional interactions between hippocampus and higher-order association cortices. In particular, those in the prefrontal and parietal regions are responsible for cognitive control, attention, and complex information integration. This reorganization may serve to mitigate impaired cognitive functions following stroke and provides a neuroanatomically grounded rationale for targeting these circuits in future neuromodulation interventions. This observation aligns with findings from Zhu et al. (37), who reported widespread reductions in both structural connectivity of the basal ganglia-frontal network and FC within the frontoparietal and cingulate-insular networks among patients with mild basal ganglia stroke in the PSCI group. These findings suggest a top-down disruption model in which damage to subcortical regions propagates dysfunction to higher-order cognitive networks. In contrast, the enhanced hippocampus-cortical connectivity observed in the present study may represent a distinct, potentially compensatory, neuroplastic response. A large-scale multicenter study identified that the structural disconnection patterns most strongly associated with global PSCI were concentrated in the left prefrontal cortex, thalamus, and basal ganglia-regions, with dense white matter projections to the higher association cortices highlighted in this study. This anatomical overlap implies that enhanced FC may emerge as a compensatory adaptation to underlying structural disconnections (38). Jung et al. (15) offered an alternative perspective, reporting reduced FC between hippocampus sub-regions and inferior parietal in PSCI patients, with the degree of reduction correlating with memory impairments. This appears to contrast with the current finding of enhanced connectivity between hippocampus and left inferior parietal as well as right supramarginal gyrus. However, this discrepancy may be attributable to differences in patient characteristics—including stroke location and chronicity—as well as variations in methodological approaches, such as hippocampus segmentation strategies or FC analysis pipelines. Such inconsistencies underscore the heterogeneity and complexity of neural mechanisms underlying PSCI. In summary, the specific enhancement of hippocampus-higher cortical FC revealed in this study shows both convergence with and divergence from existing literature. Although consistent with prior evidence highlighting the prognostic significance of hippocampus network integrity, it also reveals differential patterns of connectivity change, reflecting the multifaceted nature of post-stroke brain network reorganization. This variability is likely influenced by multiple factors, including lesion location, severity of disease, stage of recovery, and individual neurobiological differences.

In addition to the above, enhanced FC in PSCI may reflect two non-mutually exclusive mechanisms: compensatory neuroplasticity and maladaptive hyperconnectivity (39). On the one hand, enhanced hippocampal-cortical FC may represent an adaptive response, whereby spared neural circuits are recruited to maintain cognitive performance following structural damage. This is supported by positive correlations observed between FC strength and cognitive scores in the present study. On the other hand, increased FC may also reflect inefficient or pathological network reorganization, characterized by excessive synchronization and reduced network specificity. Notably, previous studies have reported decreased FC within key cognitive networks in PSCI, particularly in the DMN and frontoparietal network, suggesting disrupted network integrity (40,41). The coexistence of enhanced and decreased FC findings across studies may reflect heterogeneity in stroke stage, lesion location, and analytical approaches. Therefore, the enhanced hippocampal-prefrontal connectivity observed in this study may represent a stage-dependent or region-specific reorganization process. The preferential involvement of right frontal inferior opercular warrants further consideration. In the present cohort, most lesions were located in subcortical regions, including the basal ganglia, corona radiata, and thalamus, with relatively limited direct involvement of the inferior frontal cortex. This suggests that right frontal inferior opercular may be structurally preserved and thus available for functional reorganization. From a functional perspective, right inferior frontal gyrus is a key node in executive control and attentional networks, supporting inhibitory control and working memory processes. Given the relatively balanced lesion laterality in our cohort, increased recruitment of right hemisphere may reflect contralesional compensation (42). Moreover, cognitive domains frequently impaired in PSCI such as attention, executive function, and working memory are strongly associated with right frontal lobe function. Therefore, enhanced hippocampal connectivity with right frontal inferior opercular may represent a targeted adaptive response supporting these cognitive processes.

This study has several limitations that warrant consideration. First, the analysis did not stratify patients by AIS subtype, which may influence functional reorganization patterns and limit the generalizability of the findings. Second, the follow-up period was limited to three months post-stroke, precluding the assessment of long-term cognitive outcomes and the establishment of causal relationships between FC changes and clinical recovery. Third, the study relied solely on functional imaging without incorporating multimodal neuroimaging techniques such as diffusion tensor imaging or diffusion kurtosis imaging, which could provide complementary insights into structural connectivity and enable a more comprehensive evaluation of structure-function relationships in the post-stroke brain.

Conclusions

This study reveals a distinctive pattern of FC reorganization between the hippocampus and key cortical regions in PSCI patients. The identification of the right frontal inferior opercular as a key node in hippocampal-cortical connectivity suggests that this region may serve as a potential target for non-invasive brain stimulation techniques, such as transcranial magnetic stimulation. Network-guided stimulation strategies aimed at enhancing hippocampal-prefrontal connectivity may improve cognitive outcomes in PSCI patients. Individualized targeting based on FC profiles could represent a promising strategy and facilitate precision rehabilitation for PSCI patients.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0015/rc

Data Sharing Statement: Available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0015/dss

Funding: This work was supported by the Chronic Disease Management Research Project of the National Health Commission Capacity Building and Continuing Education Center (No. GWJJMB202510021008), DCMST.NHC Clinical Research Project (No. WKZX2025CZ0209) and Tianshan Talents Program-Innovation Leading Talent in Science and Technology (No. 2023TSYCLJ0027).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-2026-1-0015/coif). The 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 amendments. Ethical approval was granted by the Institutional Ethics Committee of The First Affiliated Hospital of Xinjiang Medical University (No. K202505-30), and all participants in this study provided informed consent.

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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