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Age-related small vessel disease: a potential contributor to neurodegeneration in multiple sclerosis.

ABSTRACT: Multiple sclerosis (MS) is a chronic inflammatory demyelinating disorder of the central nervous system wherein, after an initial phase of transient neurological defects, slow neurological deterioration due to progressive neuronal loss ensues. Age is a major determinant of MS progression onset and disability. Over the past years, several mechanisms have been proposed to explain the key drivers of neurodegeneration and disability accumulation in MS. However, the effect of commonly encountered age-related cerebral vessel disease, namely small vessel disease (SVD), has been largely neglected and constitutes the aim of this review. SVD shares some features with MS, that is, white matter demyelination and brain atrophy, and has been shown to contribute to the neuronal damage seen in vascular cognitive impairment. Several lines of evidence suggest that an interaction between MS and SVD may influence MS-related neurodegeneration. SVD may contribute to hypoperfusion, reduced vascular reactivity and tissue hypoxia, features seen in MS. Venule and endothelium abnormalities have been documented in MS but the role of arterioles and of other neurovascular unit structures, such as the pericyte, has not been explored. Vascular risk factors (VRF) have recently been associated with faster progression in MS, though the mechanisms are unclear since very few studies have addressed the impact of VRF and SVD on MS imaging and pathology outcomes. Therapeutic agents targeting the microvasculature and the neurovascular unit may impact both SVD and MS and may benefit patients with dual pathology.

SUBMITTER: Geraldes R

PROVIDER: S-EPMC8029132 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and Purpose- The role of inflammation in ischemic white matter disease is increasingly recognized, and further understanding of the pathophysiology might inform future treatment strategies. Multiple sclerosis (MS) is a chronic autoimmune condition in which inflammation plays a central role that also affects the white matter. We hypothesized that white matter injury might share common mechanisms and used statistical genetics techniques to assess whether having genetically elevated white matter hyperintensity (WMH) volume was associated with increased MS risk. Methods- We investigated the genetic association in 2 cohorts with magnetic resonance imaging-quantified ischemic white matter lesion volume (WMH in stroke; n=2797 and UK Biobank; n=8353) and 14?802 cases of MS and 26?703 controls from the International Multiple Sclerosis Genetics Consortium. We further performed individual-level polygenic risk score calculations for MS and measures of structural white matter disease in UK Biobank. Finally, we looked for evidence of overlapping risk across the whole genome. Results- There was no association of genetic variants influencing MS with WMH volume using summary statistics in the WMH in stroke cohort (relative risk score =1.014; 95% CI, 0.936-1.110) or in the UK Biobank cohort (relative risk score =1.030; 95% CI, 0.932-1.117). Conversely, assessing the contribution of single nucleotide polymorphisms significantly associated with WMH on the risk of MS there was no significant association (relative risk score =0.930; 95% CI, 0.736-1.191). There were no significant associations between polygenic risk scores calculations; these results were robust to the selection of single nucleotide polymorphisms at a range of significance thresholds. Whole genome analysis did not reveal any overlap of risk between the traits. Conclusions- Our results do not provide evidence to suggest a shared mechanism of white matter damage in ischemia and MS. We propose that inflammation acts in distinct pathways because of the differing nature of the primary insult.

Project description:Background Reliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD. Materials and methods A prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers. Results The gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients. Conclusion Brain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.

Project description:BackgroundHomozygous or compound heterozygous mutations in high temperature requirement serine peptidase A1 (HTRA1) gene are responsible for cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL). Recently, increasing evidence has shown that heterozygous HTRA1 mutations are also associated with cerebral small vessel disease (CSVD) with an autosomal dominant pattern of inheritance. This study was aimed to analyze the genetic and clinical characteristics of HTRA1-related autosomal dominant CSVD.MethodsWe presented three new Chinese cases of familial CSVD with heterozygous HTRA1 mutations and reviewed all clinical case reports and articles on HTRA1-related autosomal dominant CSVD included in PUBMED by the end of March 1, 2020. CARASIL probands with genetic diagnosis reported to date were also reviewed. The genetic and clinical characteristics of HTRA1-related autosomal dominant CSVD were summarized and analyzed by comparing with CARASIL.ResultsForty-four HTRA1-related autosomal dominant CSVD probands and 22 CARASIL probands were included. Compared with typical CARASIL, HTRA1-related autosomal dominant probands has a higher proportion of vascular risk factors (P < 0.001), a later onset age (P < 0.001), and a relatively slower clinical progression. Alopecia and spondylosis can be observed, but less than those in the typical CARASIL. Thirty-five heterozygous mutations in HTRA1 were reported, most of which were missense mutations. Amino acids located close to amino acids 250-300 were most frequently affected, followed by these located near 150∼200. While amino acids 250∼300 were also the most frequently affected region in CARASIL patients, fewer mutations precede the 200th amino acids were detected, especially in the Kazal-type serine protease domain.ConclusionsHTRA1-related autosomal dominant CSVD is present as a mild phenotype of CARASIL. The trend of regional concentration of mutation sites may be related to the concentration of key sites in these regions which are responsible for pathogenesis of HTRA1-related autosomal dominant CSVD.

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Age-related small vessel disease: a potential contributor to neurodegeneration in multiple sclerosis.

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