Home    中文  
 
  • Search
  • lucene Search
  • Citation
  • Fig/Tab
  • Adv Search
Just Accepted  |  Current Issue  |  Archive  |  Featured Articles  |  Most Read  |  Most Download  |  Most Cited

Chinese Journal of Cerebrovascular Diseases(Electronic Edition) ›› 2025, Vol. 19 ›› Issue (03): 198-206. doi: 10.3877/cma.j.issn.1673-9248.2025.03.005

Special Issue:

• Original Article • Previous Articles     Next Articles

Correlation between gait impairment, cognitive impairment, and neuroimaging features in patients with cerebral small vessel disease

Wanhu Liu1, Wei Bu2, Yujuan Dong1, Wenjun Li1, Yanan Jia1, Cuicui Liu1, Huiling Ren,1()   

  1. 1 Department of Neurology, the Third Hospital of Hebei Medical University, Shijiazhuang 050051, China
    2 Department of Neurosurgery, the Third Hospital of Hebei Medical University, Shijiazhuang 050051, China
  • Received:2024-08-22 Online:2025-06-01 Published:2025-08-11
  • Contact: Huiling Ren

Abstract:

Objective

To investigate the correlations between gait impairment, cognitive impairment, and neuroimaging features in patients with cerebral small vessel disease (CSVD).

Methods

A total of 140 patients diagnosed with CSVD by neuroimaging examinations at the Department of Neurology, the Third Hospital of Hebei Medical University from April to November 2023 were retrospectively and consecutively enrolled. CSVD burden was quantified using established imaging markers (white matter hyperintensity, lacunes, cerebral microbleeds, enlarged perivascular spaces) and summed into a total score (range: 0-4). Participants were stratified by burden severity: mild (0-1, n=61) and severe (2-4, n=79). Based on the montreal cognitive assessment (MoCA) score, patients were divided into the cognitive-impaired group (0-25 points, n=105) and the cognitive-impaired group (26-30 points, n=35). Univariate analysis (t-test and χ2 test) was used to compare differences in clinical and imaging data between different groups. Multiple linear regression analysis was applied to investigate the associations between CSVD total burden and gait/cognitive impairment. Multivariate logistic regression was used to identify independent influencing factors for cognitive impairment. Receiver operating characteristic (ROC) curve analysis was employed to evaluate diagnostic performance of key predictors.

Results

Compared to the mild-burden patients the severe-burden patients exhibited significantly slower gait speed [(0.34±0.12) m/s vs (0.47±0.11) m/s, t=6.166, P<0.001], reduced stride length [(0.39±0.13) m vs (0.45±0.10) m, t=3.054, P=0.003], lower Tinetti performance-oriented mobility assessment (POMA) score [(20.90±4.42) vs (23.95±2.44), t=4.849, P<0.001], and worse MoCA score [(20.46±5.07) vs (24.43±4.04), t=5.013, P<0.001]. Conversely, they had significantly wider stride width [(0.16±0.06) m vs (0.13±0.06) m, t=-2.950, P=0.004] longer and timed up and go (TUG) test time [(17.84±9.10) s vs (12.69±4.06) s, t=-4.115, P<0.001]. Multiple linear regression analysis showed that after adjusting for age, activities of daily living (ADL) score, and hypertension, CSVD total burden was negatively correlated with gait speed, stride length, Tinetti POMA score, and MoCA score, and positively correlated with stride width and TUG time (all P<0.05). Compared with the cognitively normal group, the cognitive impairment group had significantly higher age [(67.00±8.83) years vs (63.34±8.38) years, t=-2.149, P=0.033], homocysteine levels [(18.02±10.52) μmol/L vs (14.13±6.45) μmol/L, t=-2.017, P=0.046], proportions of moderate-to-severe white matter hyperintensity (67.6% vs 28.6%, χ2=16.415, P<0.001), proportions of cerebral microbleeds (44.8% vs 14.3%, χ2=10.443, P=0.001), and CSVD total burden score [(2.21±1.17) vs (1.23±1.09), t=4.382, P<0.001]. They also had significantly lower gait speed [(0.37±0.13) m/s vs (0.48±0.10) m/s, t=4.627, P<0.001]. Multivariate logistic regression analysis showed that after adjusting for confounding factors such as age and homocysteine levels, severe CSVD total burden (OR=1.714, 95%CI: 1.074-2.736, P<0.05) and decreased gait speed (OR=0.954, 95%CI: 0.914-0.995, P<0.05) remained significantly associated with cognitive impairment. ROC curve analysis showed that the combination of CSVD total burden and gait speed had the highest predictive efficacy for cognitive impairment in CSVD patients, with an area under the curve (AUC) of 0.779 (95%CI: 0.695-0.863).

Conclusion

Severe CSVD burden leads to gait and cognitive impairment in CSVD patients, increasing fall risk. Integrating neuroimaging burden quantification with gait speed assessment provides a clinically viable strategy for early identification of cognitive impairment in CSVD patients.

Key words: Cerebral small vessel disease, Total burden, Gait impairment, Cognitive impairment

京ICP 备07035254号-20
Copyright © Chinese Journal of Cerebrovascular Diseases(Electronic Edition), All Rights Reserved.
Tel: 01082266456, 15611963912, 15611963911 E-mail: zhnxgbzzbysy@163.com
Powered by Beijing Magtech Co. Ltd