Objective biomarkers for clinical relapse in multiple sclerosis: a metabolomics approach

A rapid review of differences in cerebrospinal neurofilament light levels in clinical subtypes of progressive multiple sclerosis

Background

Multiple sclerosis (MS) is divided into three clinical phenotypes: relapsing-remitting MS (RRMS), secondary progressive MS (SPMS), and primary progressive MS (PPMS). It is unknown to what extent SPMS and PPMS pathophysiology share inflammatory or neurodegenerative pathological processes. Cerebrospinal (CSF) neurofilament light (NfL) has been broadly studied in different MS phenotypes and is a candidate biomarker for comparing MS subtypes.

Research question

Are CSF NfL levels different among clinical subtypes of progressive MS?

Methods

A search strategy identifying original research investigating fluid neurodegenerative biomarkers in progressive forms of MS between 2010 and 2022 was applied to Medline. Identified articles underwent title and abstract screen and full text review against pre-specified criteria. Data abstraction was limited to studies that measured NfL levels in the CSF. Reported statistical comparisons of NfL levels between clinical phenotypes were abstracted qualitatively.

Results

18 studies that focused on investigating direct comparisons of CSF NfL from people with MS were included in the final report. We found NfL levels were typically reported to be higher in relapsing and progressive MS compared to healthy controls. Notably, higher NfL levels were not clearly associated with progressive MS subtypes when compared to relapsing MS, and there was no observed difference in NfL levels between PPMS and SPMS in articles that separately assessed these phenotypes.

Conclusion

CSF NfL levels distinguish individuals with MS from healthy controls but do not differentiate MS subtypes. Broad biological phenotyping is needed to overcome limitations of current clinical phenotyping and improve biomarker translatability to decision-making in the clinic.

Early urinary candidate biomarkers and clinical outcomes of intervention in a rat model of experimental autoimmune encephalomyelitis

Multiple sclerosis is a chronic autoimmune demyelinating disease of the central nervous system and is difficult to diagnose in early stages. Without homeostatic control, urine was reported to have the ability to accumulate early changes in the body. We expect that urinary proteome can reflect early changes in the nervous system. The early urinary proteome changes in a most employed multiple sclerosis rat model (experimental autoimmune encephalomyelitis) were analysed to explore early urinary candidate biomarkers, and early treatment of methylprednisolone was used to evaluate the therapeutic effect. Twenty-five urinary proteins were altered at day 7 when there were no clinical symptoms and obvious histological changes. Fourteen were reported to be differently expressed in the serum/cerebrospinal fluid/brain tissues of multiple sclerosis patients or animals such as angiotensinogen and matrix metallopeptidase 8. Functional analysis showed that the dysregulated proteins were associated with asparagine degradation, neuroinflammation and lipid metabolism. After the early treatment of methylprednisolone, the incidence of encephalomyelitis in the intervention group was only 1/13. This study demonstrates that urine may be a good source of biomarkers for the early detection of multiple sclerosis. These findings may provide important information for early diagnosis and intervention of multiple sclerosis in the future.

A metabolome-wide Mendelian randomization study prioritizes potential causal circulating metabolites for multiple sclerosis

To prioritize circulating metabolites that likely play causal roles in the pathogenesis of multiple sclerosis (MS). Two-sample Mendelian randomization analysis was performed to estimate the causal effects of 571 circulating metabolites on the risk of MS. Genetic instruments for circulating metabolites were obtained from three previous genome-wide association studies (GWAS) of the blood metabolome (N = 7824; 24,925; and 115,078; respectively), while genetic associations with MS were from a large GWAS by the International Multiple Sclerosis Genetics Consortium (14,802 cases and 26,703 control). The primary analysis was performed with the multiplicative random-effect inverse variance-weighted method, while multiple sensitivity analyses were conducted with the weighted median, weighted mode, MR-Egger, and MR-PRESSO. A total of 29 metabolites had suggestive evidence of causal associations with MS. Genetically instrumented levels of serine (OR = 1.56, 95% CI = 1.25-1.95), lysine (OR = 1.18, 95% CI = 1.01-1.38), acetone (OR = 2.45, 95% CI = 1.02-5.90), and acetoacetate (OR = 2.47, 95% CI = 1.14-5.34) were associated with a higher MS risk. Total cholesterol and phospholipids in large very-low-density lipoprotein were associated with a lower MS risk (OR = 0.83, 95% CI = 0.69-1.00; OR = 0.80, 95% CI = 0.68-0.95), but risk-increasing associations (OR = 1.20, 95% CI = 1.04-1.40; OR = 1.13, 95% CI = 1.00-1.28) were observed for the same two lipids in very large high-density lipoprotein. Our metabolome-wide Mendelian randomization study prioritized a list of circulating metabolites, such as serine, lysine, acetone, acetoacetate, and lipids, that likely have causal associations with MS.

Metabolomics detects clinically silent neuroinflammatory lesions earlier than neurofilament-light chain in a focal multiple sclerosis animal model

Background

Despite widespread searches, there are currently no validated biofluid markers for the detection of subclinical neuroinflammation in multiple sclerosis (MS). The dynamic nature of human metabolism in response to changes in homeostasis, as measured by metabolomics, may allow early identification of clinically silent neuroinflammation. Using the delayed-type hypersensitivity (DTH) MS rat model, we investigated the serum and cerebrospinal fluid (CSF) metabolomics profiles and neurofilament-light chain (NfL) levels, as a putative marker of neuroaxonal damage, arising from focal, clinically silent neuroinflammatory brain lesions and their discriminatory abilities to distinguish DTH animals from controls.

Methods

¹H nuclear magnetic resonance (NMR) spectroscopy metabolomics and NfL measurements were performed on serum and CSF at days 12, 28 and 60 after DTH lesion initiation. Supervised multivariate analyses were used to determine metabolomics differences between DTH animals and controls. Immunohistochemistry was used to assess the extent of neuroinflammation and tissue damage.

Results

Serum and CSF metabolomics perturbations were detectable in DTH animals (vs. controls) at all time points, with the greatest change occurring at the earliest time point (day 12) when the neuroinflammatory response was most intense (mean predictive accuracy [SD]—serum: 80.6 [10.7]%, p < 0.0001; CSF: 69.3 [13.5]%, p < 0.0001). The top discriminatory metabolites at day 12 (serum: allantoin, cytidine; CSF: glutamine, glucose) were all reduced in DTH animals compared to controls, and correlated with histological markers of neuroinflammation, particularly astrogliosis (Pearson coefficient, r—allantoin: r = − 0.562, p = 0.004; glutamine: r = − 0.528, p = 0.008). Serum and CSF NfL levels did not distinguish DTH animals from controls at day 12, rather, significant differences were observed at day 28 (mean [SEM]—serum: 38.5 [4.8] vs. 17.4 [2.6] pg/mL, p = 0.002; CSF: 1312.0 [379.1] vs. 475.8 [74.7] pg/mL, p = 0.027). Neither serum nor CSF NfL levels correlated with markers of neuroinflammation; serum NfL did, however, correlate strongly with axonal loss (r = 0.641, p = 0.001), but CSF NfL did not (p = 0.137).

Conclusions

While NfL levels were elevated later in the pathogenesis of the DTH lesion, serum and CSF metabolomics were able to detect early, clinically silent neuroinflammation and are likely to present sensitive biomarkers for the assessment of subclinical disease activity in patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12974-022-02614-8.

Current and Future Biomarkers in Multiple Sclerosis

Multiple sclerosis (MS) is a debilitating autoimmune disorder. Currently, there is a lack of effective treatment for the progressive form of MS, partly due to insensitive readout for neurodegeneration. The recent development of sensitive assays for neurofilament light chain (NfL) has made it a potential new biomarker in predicting MS disease activity and progression, providing an additional readout in clinical trials. However, NfL is elevated in other neurodegenerative disorders besides MS, and, furthermore, it is also confounded by age, body mass index (BMI), and blood volume. Additionally, there is considerable overlap in the range of serum NfL (sNfL) levels compared to healthy controls. These confounders demonstrate the limitations of using solely NfL as a marker to monitor disease activity in MS patients. Other blood and cerebrospinal fluid (CSF) biomarkers of axonal damage, neuronal damage, glial dysfunction, demyelination, and inflammation have been studied as actionable biomarkers for MS and have provided insight into the pathology underlying the disease process of MS. However, these other biomarkers may be plagued with similar issues as NfL. Using biomarkers of a bioinformatic approach that includes cellular studies, micro-RNAs (miRNAs), extracellular vesicles (EVs), metabolomics, metabolites and the microbiome may prove to be useful in developing a more comprehensive panel that addresses the limitations of using a single biomarker. Therefore, more research with recent technological and statistical approaches is needed to identify novel and useful diagnostic and prognostic biomarker tools in MS.

Determination of CSF GFAP, CCN5, and vWF Levels Enhances the Diagnostic Accuracy of Clinically Defined MS From Non-MS Patients With CSF Oligoclonal Bands

Background

Inclusion of cerebrospinal fluid (CSF) oligoclonal IgG bands (OCGB) in the revised McDonald criteria increases the sensitivity of diagnosis when dissemination in time (DIT) cannot be proven. While OCGB negative patients are unlikely to develop clinically definite (CD) MS, OCGB positivity may lead to an erroneous diagnosis in conditions that present similarly, such as neuromyelitis optica spectrum disorders (NMOSD) or neurosarcoidosis.

Objective

To identify specific, OCGB-complementary, biomarkers to improve diagnostic accuracy in OCGB positive patients.

Methods

We analysed the CSF metabolome and proteome of CDMS (n=41) and confirmed non-MS patients (n=64) comprising a range of CNS conditions routinely encountered in neurology clinics.

Results

OCGB discriminated between CDMS and non-MS with high sensitivity (85%), but low specificity (67%), as previously described. Machine learning methods revealed CCN5 levels provide greater accuracy, sensitivity, and specificity than OCGB (79%, +5%; 90%, +5%; and 72%, +5% respectively) while glial fibrillary acidic protein (GFAP) identified CDMS with 100% specificity (+33%). A multiomics approach improved accuracy further to 90% (+16%).

Conclusion

The measurement of a few additional CSF biomarkers could be used to complement OCGB and improve the specificity of MS diagnosis when clinical and radiological evidence of DIT is absent.

Recommendations for the Diagnosis and Treatment of Multiple Sclerosis Relapses

Minimizing the risk of relapse is essential in multiple sclerosis (MS). As none of the treatments currently available are capable of completely preventing relapses, treatment of these episodes remains a cornerstone of MS care. The objective of this manuscript is to reduce uncertainty and improve quality of care of this neurological process. This article addresses definitions of key concepts, recommendations for clinical examination, classification criteria, magnetic resonance imaging, biomarkers, and specific therapeutic counsels including special populations such as pregnant and breastfeeding women, and children. An algorithm for treating MS relapses is also provided.