Prediction of Conversion from Clinically Isolated Syndrome to Multiple Sclerosis According to Baseline Characteristics: A Prospective Study

Cerebellar and/or Brainstem Lesions Indicate Poor Prognosis in Multiple Sclerosis: A Systematic Review

Multiple sclerosis is a serious neurological disease that affects millions of people worldwide. Cerebellar and brainstem symptoms are common in the course of multiple sclerosis, but their prognostic value is unclear. This systematic review aimed to determine the relationship between the location of lesions in the cerebellum and/or brainstem and the prognosis in multiple sclerosis. In this systematic review, we searched and comprehensively read articles related to this research topic in Chinese and English electronic databases (PubMed, Embase, Cochrane Library, CNKI, and CBM) using search terms “multiple sclerosis,” “cerebellum,” “brainstem,” “prognosis,” and others. Cerebellar and brainstem clinically isolated syndromes and clinically definite multiple sclerosis were important predictors of transformation (hazard ratio, 2.58; 95% confidence interval, 1.58–4.22). Cerebellar and/or brainstem lesions indicate a poor overall prognosis in multiple sclerosis, but because of inconsistency, more clinical data are needed.

Integrative biochemical, proteomics and metabolomics cerebrospinal fluid biomarkers predict clinical conversion to multiple sclerosis

Eighty-five percent of multiple sclerosis cases begin with a discrete attack termed clinically isolated syndrome, but 37% of clinically isolated syndrome patients do not experience a relapse within 20 years of onset. Thus, the identification of biomarkers able to differentiate between individuals who are most likely to have a second clinical attack from those who remain in the clinically isolated syndrome stage is essential to apply a personalized medicine approach. We sought to identify biomarkers from biochemical, metabolic and proteomic screens that predict clinically defined conversion from clinically isolated syndrome to multiple sclerosis and generate a multi-omics-based algorithm with higher prognostic accuracy than any currently available test. An integrative multi-variate approach was applied to the analysis of cerebrospinal fluid samples taken from 54 individuals at the point of clinically isolated syndrome with 2-10 years of subsequent follow-up enabling stratification into clinical converters and non-converters. Leukocyte counts were significantly elevated at onset in the clinical converters and predict the occurrence of a second attack with 70% accuracy. Myo-inositol levels were significantly increased in clinical converters while glucose levels were decreased, predicting transition to multiple sclerosis with accuracies of 72% and 63%, respectively. Proteomics analysis identified 89 novel gene products related to conversion. The identified biochemical and protein biomarkers were combined to produce an algorithm with predictive accuracy of 83% for the transition to clinically defined multiple sclerosis, outperforming any individual biomarker in isolation including oligoclonal bands. The identified protein biomarkers are consistent with an exaggerated immune response, perturbed energy metabolism and multiple sclerosis pathology in the clinical converter group. The new biomarkers presented provide novel insight into the molecular pathways promoting disease while the multi-omics algorithm provides a means to more accurately predict whether an individual is likely to convert to clinically defined multiple sclerosis.

Two Classes of T1 Hypointense Lesions in Multiple Sclerosis With Different Clinical Relevance

Background: Hypointense lesions on T1-weighted images have important clinical relevance in multiple sclerosis patients. Traditionally, spin-echo (SE) sequences are used to assess these lesions (termed black holes), but Fast Spoiled Gradient-Echo (FSPGR) sequences provide an excellent alternative.

Objective: To determine whether the contrast difference between T1 hypointense lesions and the surrounding normal white matter is similar on the two sequences, whether different lesion types could be identified, and whether the clinical relevance of these lesions types are different.

Methods: Seventy-nine multiple sclerosis patients' lesions were manually segmented, then registered to T1 sequences. Median intensity values of lesions were identified on all sequences, then K-means clustering was applied to assess whether distinct clusters of lesions can be defined based on intensity values on SE, FSPGR, and FLAIR sequences. The standardized intensity of the lesions in each cluster was compared to the intensity of the normal appearing white matter in order to see if lesions stand out from the white matter on a given sequence.

Results: 100% of lesions on FSPGR images and 69% on SE sequence in cluster #1 exceeded a standardized lesion distance of Z = 2.3 (p < 0.05). In cluster #2, 78.7% of lesions on FSPGR and only 17.7% of lesions on SE sequence were above this cutoff value, meaning that these lesions were not easily seen on SE images. Lesion count in the second cluster (lesions less identifiable on SE) significantly correlated with the Expanded Disability Status Scale (EDSS) (R: 0.30, p ≤ 0.006) and with disease duration (R: 0.33, p ≤ 0.002).

Conclusion: We showed that black holes can be separated into two distinct clusters based on their intensity values on various sequences, only one of which is related to clinical parameters. This emphasizes the joint role of FSPGR and SE sequences in the monitoring of MS patients and provides insight into the role of black holes in MS.

Conversion of clinically isolated syndrome to multiple sclerosis: a prospective multi-center study in Eastern India

BACKGROUND

In White populations more than 60% of clinically isolated syndrome (CIS) convert to multiple sclerosis (MS) on a long-term follow-up; several predictors for conversion have been identified.

OBJECTIVE

This study aimed to determine the conversion rate and the predictors of conversion from CIS to MS (McDonald 2010) among Indians. The other objective was to evaluate the diagnostic accuracy of the new McDonald 2017 criteria in prediction of a second clinical attack.

METHODS

Clinical and demographic data of CIS cohorts were collected. Baseline investigations included cerebrospinal magnetic resonance imaging (MRI) with contrast and cerebrospinal fluid (CSF) testing for oligoclonal band (OCB). Follow-up clinical and MRI examinations were performed annually for at least 24 months.

RESULTS

Of the 82 subjects (age range 15-58 years), 36 (43.9%) converted to MS; 31/82 (37.8%) converted in 24 months. The predictors for conversion were earlier age of onset, CSF-OCB, cerebral MRI T2 lesion count, and periventricular and juxtacortical location of lesions. Twenty-two (26.83%) CIS fulfilled the McDonald MS 2017 criteria at baseline.

CONCLUSION

In this first prospective study of CIS in India, the risk factors for conversion are similar but the conversion rate to MS is lower than that in the western nations.