Glymphatic dysfunction in multiple sclerosis and its association with disease pathology and disability

Abstract

Background: The role of the glymphatic system in multiple sclerosis (MS)-related disability remains underexplored. Diffusion-tensor image analysis along the perivascular space (DTI-ALPS) offers a non-invasive method to assess glymphatic function.

Objective: To evaluate glymphatic function in MS patients with lower and higher disability.

Methods: This study included 118 MS patients who underwent structural, diffusion-weighted imaging, and clinical assessment. The participants were divided into lower (MS-L, n = 57) and higher disability (MS-H, n = 61) subgroups. Brain parenchymal fraction (BPF), lesion load (LL), and DTI-ALPS index were measured. Subgroup differences and correlations between DTI-ALPS index and other measures were explored. Logistic regression was performed to evaluate BPF, LL, and DTI-ALPS index in classifying lower and higher disability patients.

Results: Significant differences in DTI-ALPS index between MS-H and MS-L (d = -0.71, false discovery rate-corrected p-value (p-FDR) = 0.001) were found. The DTI-ALPS index correlated significantly with disease duration (r_p = -0.29, p-FDR = 0.002) and EDSS (r_sp = -0.35, p-FDR = 0.0002). It also showed significant correlations with BPF and LL. DTI-ALPS index and LL were significant predictors of disability subgroup (DTI-ALPS: odds ratio (OR) = 1.77, p = 0.04, LL: OR = 0.94, p = 0.02).

Conclusion: Our findings highlight DTI-ALPS index as an imaging biomarker in MS, suggesting the involvement of glymphatic impairment in MS pathology, although further research is needed to elucidate its role in contributing to MS-related disability.

Keywords: DTI-ALPS; Multiple sclerosis; disability; glymphatic system; neurodegeneration; neuroinflammation.

Figure 1.

Group differences in brain parenchymal fraction, lesion load, and mean DTI-ALPS index. Raincloud plots depicting the distribution and differences between the higher (MS-H, green) and the lower (MS-L, orange) disability subgroups. Differences were calculated using ANCOVA adjusting for age and sex. (a) Brain parenchymal fraction (BPF), (b) lesion load, and (c) mean DTI-ALPS.

Figure 2.

Paired differences between left and right DTI-ALPS indices and correlation between mean DTI-ALPS index and age. (a) Right and left DTI-ALPS index measurements differences estimated using paired samples t-test and (b) correlation between mean DTI-ALPS index and age. Blue dashes represent the confidence interval, and green dashes represent the prediction intervals.

Figure 3.

Correlations with disease duration and EDSS. (a) Pearson’s correlation calculated between mean DTI-ALPS index and disease duration adjusting for sex and (b) Spearman’s correlation calculated between DTI-ALPS index and EDSS adjusting for age and sex. Blue dashes represent the confidence interval, and green dashes represent the prediction intervals.

Figure 4.

Correlations with imaging measures. Pearson’s partial correlations adjusting for age and sex between mean DTI-ALPS index and (a) brain parenchymal fraction (BPF), (b) gray matter fraction (GMF), (c) deep gray matter (DGM), (d) mean cortical thickness (CTh), (e) white matter fraction (WMF), and (f) lesion load. Blue dashes represent the confidence interval, and green dashes represent the prediction intervals.

Figure 5.

Multivariable logistic regression receiver-operating curve. ROC curve assessing the combined performance of the mean DTI-ALPS index, brain parenchymal fraction, and lesion load in predicting disability level.

Figure 6.

Independent receiver-operating curves for the mean DTI-ALPS index, brain parenchymal fraction, and lesion load. ROC curves assessing the performance of each imaging measure in predicting disability level.