Emerging imaging and liquid biomarkers in multiple sclerosis

Emerging imaging markers in radiologically isolated syndrome: implications for earlier treatment initiation

The presence of central nervous system lesions fulfilling the criteria of dissemination in space and time on MRI leads to the diagnosis of a radiologically isolated syndrome (RIS), which may be an early sign of multiple sclerosis (MS). However, some patients who do not fulfill the necessary criteria for RIS still evolve to MS, and some T2 hyperintensities that resemble demyelinating lesions may originate from mimics. In light of the recent recognition of the efficacy of disease-modifying therapy (DMT) in RIS, it is relevant to consider additional imaging features that are more specific of MS. We performed a narrative review on cortical lesions (CL), the central vein sign (CVS), and paramagnetic rim lesions (PRL) in patients with RIS. In previous RIS studies, the reported prevalence of CLs ranges between 20.0 and 40.0%, CVS + white matter lesions (WMLs) between 87.0 and 93.0% and PRLs between 26.7 and 63.0%. Overall, these imaging findings appear to be frequent in RIS cohorts, although not consistently taken into account in previous studies. The search for CLs, CVS + WML and PRLs in RIS patients could lead to earlier identification of patients who will evolve to MS and benefit from DMTs.

Multi-organ imaging-derived polygenic indexes for brain and body health

The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.

Modeling and simulation for prediction of multiple sclerosis progression

In light of extensive work that has created a wide range of techniques for predicting the course of multiple sclerosis (MS) disease, this paper attempts to provide an overview of these approaches and put forth an alternative way to predict the disease progression. For this purpose, the existing methods for estimating and predicting the course of the disease have been categorized into clinical, radiological, biological, and computational or artificial intelligence-based markers. Weighing the weaknesses and strengths of these prognostic groups is a profound method that is yet in need and works directly at the level of diseased connectivity. Therefore, we propose using the computational models in combination with established connectomes as a predictive tool for MS disease trajectories. The fundamental conduction-based Hodgkin-Huxley model emerged as promising from examining these studies. The advantage of the Hodgkin-Huxley model is that certain properties of connectomes, such as neuronal connection weights, spatial distances, and adjustments of signal transmission rates, can be taken into account. It is precisely these properties that are particularly altered in MS and that have strong implications for processing, transmission, and interactions of neuronal signaling patterns. The Hodgkin-Huxley (HH) equations as a point-neuron model are used for signal propagation inside a small network. The objective is to change the conduction parameter of the neuron model, replicate the changes in myelin properties in MS and observe the dynamics of the signal propagation across the network. The model is initially validated for different lengths, conduction values, and connection weights through three nodal connections. Later, these individual factors are incorporated into a small network and simulated to mimic the condition of MS. The signal propagation pattern is observed after inducing changes in conduction parameters at certain nodes in the network and compared against a control model pattern obtained before the changes are applied to the network. The signal propagation pattern varies as expected by adapting to the input conditions. Similarly, when the model is applied to a connectome, the pattern changes could give an insight into disease progression. This approach has opened up a new path to explore the progression of the disease in MS. The work is in its preliminary state, but with a future vision to apply this method in a connectome, providing a better clinical tool.

Multiple Sclerosis: From the Application of Oligoclonal Bands to Novel Potential Biomarkers

Multiple sclerosis is a chronic immune-mediated disorder of the central nervous system with a high heterogeneity among patients. In the clinical setting, one of the main challenges is a proper and early diagnosis for the prediction of disease activity. Current diagnosis is based on the integration of clinical, imaging, and laboratory results, with the latter based on the presence of intrathecal IgG oligoclonal bands in the cerebrospinal fluid whose detection via isoelectric focusing followed by immunoblotting represents the gold standard. Intrathecal synthesis can also be evidenced by the measurement of kappa free light chains in the cerebrospinal fluid, which has reached similar diagnostic accuracy compared to that of oligoclonal bands in the identification of patients with multiple sclerosis; moreover, recent studies have also highlighted its value for early disease activity prediction. This strategy has significant advantages as compared to using oligoclonal band detection, even though some issues remain open. Here, we discuss the current methods applied for cerebrospinal fluid analysis to achieve the most accurate diagnosis and for follow-up and prognosis evaluation. In addition, we describe new promising biomarkers, currently under investigation, that could contribute both to a better diagnosis of multiple sclerosis and to its monitoring of the therapeutic treatment response.

Serum biomarker levels predict disability progression in patients with primary progressive multiple sclerosis

BACKGROUND

We aimed to investigate the potential of serum biomarker levels to predict disability progression in a multicentric real-world cohort of patients with primary progressive multiple sclerosis (PPMS).

METHODS

A total of 141 patients with PPMS from 18 European MS centres were included. Disability progression was investigated using change in Expanded Disability Status Scale (EDSS) score over three time intervals: baseline to 2 years, 6 years and to the last follow-up. Serum levels of neurofilament light chain (sNfL), glial fibrillar acidic protein (sGFAP) and chitinase 3-like 1 (sCHI3L1) were measured using single-molecule array assays at baseline. Correlations between biomarker levels, and between biomarkers and age were quantified using Spearman's r. Univariable and multivariable linear models were performed to assess associations between biomarker levels and EDSS change over the different time periods.

RESULTS

Median (IQR) age of patients was 52.9 (46.4-58.5) years, and 58 (41.1%) were men. Median follow-up time was 9.1 (7.0-12.6) years. Only 8 (5.7%) patients received treatment during follow-up. sNfL and sGFAP levels were moderately correlated (r=0.43) and both weakly correlated with sCHI3L1 levels (r=0.19 and r=0.17, respectively). In multivariable analyses, levels of the three biomarkers were associated with EDSS changes across all time periods. However, when analysis was restricted to non-inflammatory patients according to clinical and radiological parameters (n=64), only sCHI3L1 levels remained associated with future EDSS change.

CONCLUSIONS

Levels of sNfL, sGFAP and sCHI3L1 are prognostic biomarkers associated with disability progression in patients with PPMS, being CHI3L1 findings less dependent on the inflammatory component associated with disease progression.

VISIBL-MS: A bilingual educational framework to increase awareness of early multiple sclerosis

BACKGROUND

Despite advancements in treatments of multiple sclerosis (MS), there is a lack of awareness of early MS symptoms, especially in students and the public, contributing to delays in diagnosis and treatment. This review aims to identify gaps in tools to increase awareness and to provide a bilingual framework to facilitate recognition of early MS symptoms.

METHODS

We performed a literature review to determine the use of English and Spanish mnemonics in MS education for medical students and patients.

RESULTS

There is no educational tool to help remember the early signs of MS at present. Here we present a framework for early awareness encompassed in the bilingual mnemonics VISIBLY (English) and VISIBLE (Spanish). VISIBLY stands for (1) Vision changes: Painful vision loss, loss of color vision or double vision; (2) Belly or Back numbness and Balance issues; (3) Limb weakness or Numbness; (4), Young people. Spanish version is included in the manuscript.

CONCLUSION

We posit that VISIBL-MS provides a framework for MS awareness that addresses the interconnection between language, culture, health literacy, and health outcomes and can be a useful educational tool to tackle the effects of health literacy on diverse communities.

Emerging biomarkers for improving pregnancy planning in multiple sclerosis

Background

Patient disability, relapse rate, and age are used for family planning in multiple sclerosis (MS). However, the need for more accurate biomarkers is widely recognized. We aimed to explore the influence of age on neurofilament light chain (sNfL), which reflects acute inflammation; glial fibrillary acidic protein (GFAP), associated with disability progression independent of relapses; and anti-Müllerian hormone (AMH), reflecting ovarian reserve, to provide a tailored family planning strategy.

Methods

This case-control study included 95 MS patients and 61 healthy control women (HCW). sNfL and GFAP levels were measured using a sensitive single-molecule array assay. AMH levels were measured by the automated Elecsys® Anti-Müllerian Hormone Assay.

Results

We observed no significant differences in AMH values between MS patients and the control group within any of the age-matched categories. Age exhibited a negative correlation with AMH values in both groups, as expected. Nevertheless, our findings suggest a slight tendency toward reduced ovarian reserve in MS patients (rho MS patients = -0.67, p < 0.0001; rho HCW = -0.43, p = 0.0006). Interestingly, among the 76 MS participants under 40 years old, we identified ten individuals (13.1%) with AMH levels below 0.7 ng/ml, indicative of a low ovarian reserve, and an additional six individuals (7.8%) with AMH levels between 0.7 ng/ml and 0.9 ng/ml, suggesting a potential risk of premature ovarian failure. Conversely, sNfL and GFAP levels in the MS group exhibited high variability but showed no significant association with age intervals.

Conclusion

We found no significant differences in AMH, sNfL or GFAP values between MS patients and the control group within any of the age-matched categories. The assessment of AMH, sNFL and GFAP levels at MS onset facilitates personalized therapeutic and family planning strategies for childbearing-age women.