Correlation of clinical courses and pathophysiological types

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Correlation of clinical courses and pathophysiological types

https://onlinelibrary.wiley.com/doi/ful ... acn3.50903


Discussion

Prior histopathological studies of relapsing and progressive MS central nervous system (CNS) tissues have described pathological heterogeneity.12, 13 However, CNS tissue cannot be accessed easily and safely in living PwMS. A major goal of this study was to use the relative pathological differences among MS clinical subtypes, based on published studies of neuropathology, to investigate the ability of DBSI (Diffusion basis spectrum imaging) as a noninvasive imaging method to differentiate components of MS pathology, and to determine which DBSI metrics were most important in this regard.

We previously showed in preclinical models, and autopsied and biopsied human tissue that DBSI restricted isotropic fraction correlated significantly and positively with cellularity, and fiber fraction had a significant positive correlation with axonal density.2, 3, 5, 7 The current study suggests that DBSI may be useful in living individuals with MS. The clinical subtypes of 64% and 67% of PwMS in our study were predicted by recursive partitioning of DBSI‐derived metrics of WM lesions and of normal‐appearing CC, respectively. It is noteworthy that our recursive partitioning was solely based on DBSI metrics and did not include any demographic or clinical metrics such as age, gender, disease duration, and EDSS, all of which may contribute to differentiating MS subtypes clinically.

We found that restricted isotropic fraction and fiber fraction of WM lesions were the most important DBSI metrics when using brain WM lesions to predict MS clinical subtypes. It is noteworthy that DBSI metrics performed better than T2‐weighted lesion volume at discerning clinical subtypes in our study, although prior studies have often reported higher T2 lesion loads to be associated with SPMS versus RRMS. Out of 22 subjects designated as RRMS clinically, 15 were predicted to have RRMS by DBSI metrics, 6 were predicted to have PPMS and only one was predicted to have SPMS. These results suggest that RRMS WM lesions in our subjects were more similar to PPMS than SPMS in terms of axon density and cellularity.

When analyzing DBSI metrics of normal‐appearing CC, the most significant factor to predict MS subtype was fiber fraction, with radial diffusivity and nonrestricted isotropic fraction also of importance. Based on the known pathology of SPMS, it was not unexpected that those predicted to be SPMS had the lowest fiber fractions (representing apparent axonal density) within normal‐appearing CC. Compared to RRMS, SPMS was also predicted by greater apparent demyelination based on higher DBSI‐derived radial diffusivity, also not unexpected. A higher nonrestricted isotropic fraction in normal‐appearing CC predicted RRMS over PPMS. This is possibly due to more edema in the RRMS group, about 15% of whom had gadolinium‐enhancing brain lesions (albeit not all in the CC). In contrast, only 7% of PPMS and none of the SPMS subjects given gadolinium displayed an enhancing brain lesion.

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Overall, this study provides preliminary evidence supporting the ability of DBSI as a potential noninvasive biomarker of MS neuropathology. Future studies are needed to evaluate the utility of DBSI as a potential outcome measure in trials of remyelinating and neuroprotective agents.