Misplaced Pages

Biomarkers of multiple sclerosis

Article snapshot taken from Wikipedia with creative commons attribution-sharealike license. Give it a read and then ask your questions in the chat. We can research this topic together.

Several biomarkers for diagnosis of multiple sclerosis, disease evolution and response to medication (current or expected) are under research. While most of them are still under research, there are some of them already well stablished:

  • oligoclonal bands: They present proteins that are in the CNS or in blood. Those that are in CNS but not in blood suggest a diagnosis of MS.
  • MRZ-Reaction: A polyspecific antiviral immune response against the viruses of measles, rubella and zoster found in 1992. In some reports the MRZR showed a lower sensitivity than OCB (70% vs. 100%), but a higher specificity (69% vs. 92%) for MS.
  • free light chains (FLC). Several authors have reported that they are comparable or even better than oligoclonal bands.

They can be of several types like body fluid biomarkers, imaging biomarkers or genetic biomarkers. They are expected to play an important role in the near future of MS.

Classification

Biomarkers can be classified according to several criteria. It is common to classify them according to their source (imaging biomarkers, body fluid biomarkers and genetic biomarkers) or their utility (diagnosis, evolution and response to medication)

Among the imaging biomarkers in MS the most known is MRI by two methods, gadolinium contrast and T2-hypertense lesions, but also important are PET and OCT.

Among the body fluid biomarkers the most known are oligoclonal bands in CSF but several others are under research.

Genetic biomarkers are under study but there is nothing conclusive still.

Addressing the classification by its utility we have diagnosis biomarkers, evolution biomarkers and response to medication biomarkers.

Biomarkers for diagnosis

Apart from its possible involvement in disease pathogenesis, vitamin D has been proposed as a biomarker of the disease evolution.

Diagnosis of MS has always been made by clinical examination, supported by MRI or CSF tests. According with both the pure autoimmune hypothesis and the immune-mediated hypothesis, researchers expect to find biomarkers able to yield a better diagnosis, and able to predict the response to the different available treatments.

As of 2016 no specific biomarker for MS has been found, but several studies are trying to find one. Some researchers are focusing also in specific diagnosis for each of the clinical courses

Some people focus on blood tests, given the easy availability for diagnosis. Among the studies for blood tests, the highest sensitivity and specificity reported to date is testing circulating erythrocytes (s=98.3%, e=89.5%). Also a good result was obtained using methylation patterns of circulating cell debris are specific for a number of conditions, including RRMS There are ongoing efforts to be able to diagnose MS by analysing myelin debris into the blood stream.

As of 2014, the only fully specific biomarkers found were four proteins in the CSF: CRTAC-IB (cartilage acidic protein), tetranectin (a plasminogen-binding protein), SPARC-like protein (a calcium binding cell signalling glycoprotein), and autotaxin-T (a phosphodiesterase) This list was expanded in 2016, with three CSF proteins (Immunoglobulins) reported specific for MS. They are the following immunoglobulins: Ig γ-1 (chain C region), Ig heavy chain V-III (region BRO) and Ig-κ-chain (C region)

For existing damage and disease evolution

During a clinical trial for one of the main MS drugs, a catheter was inserted into the brain's ventricles of the patients. Existing damage was evaluated and correlated with body fluids. Thanks to the courage of these volunteers, now we know that in PPMS, neurofilament light chain (NF-L) level, in CSF and serum, is a sensitive and specific marker for white matter axonal injury

About biomarkers for MRI images, Radial Diffusivity has been suggested as a biomarker associated with the level of myelination in MS lesions. However, it is affected also by tissue destruction, which may lead to exaggeration of diffusivity measures. Diffusivity can be more accurate. Distinct patterns of diffusivity in MS lesions suggest that axonal loss dominates in the T1 hypointense core and that the effects of de/remyelination may be better detected in the "T2-rim", where there is relative preservation of structural integrity.

Glial fibrillary acidic protein (GFAP) has been indicated as a possible biomarker for the progression of MS. The blood level of GFAP increases when astrocytes are damaged or activated, and elevated levels of the protein's cellular component correlate with severity of MS symptoms.

Treatments and response to therapy

Currently the only clear biomarker that predicts a response to therapy is the presence of anti-MOG autoantibodies in blood. Anti-MOG seropositive patients do not respond to approved MS medications. In fact, it seems that MS patients with anti-MOG positivity could be considered a different disease in the near future.

Comparative Effectiveness Research (CER) is an emerging field in Multiple Sclerosis treatment. The response of the disease to the different available medications at this moment cannot be predicted, and would be desirable.

But the ideal target is to find subtypes of the disease that respond better to a specific treatment. A good example could be the discovery that the presence of a gene called SLC9A9 appears in people who fail to respond to interferon β therapy or that the disregulation of some transcription factors define molecular subtypes of the disease Other good example could be the Hellberg-Eklund score for predicting the response to Natalizumab.

Though biomarkers are normally assumed to be chemical compounds in body fluids, image can also be considered a biomarker. For an example about research in this area, it has been found that fingolimod is specially suitable for patients with frequently relapsing spinal cord lesions with open-ring enhancement. Anyway, patients with spinal cord lesions could have different T-helper cells patterns that those with brain lesions.

Biomarkers are also important for the expected response to therapy. As an example of the current research, in 2000 was noticed that patients with pattern II lesions were dramatically responsive to plasmapheresis, and in February 2016, it was granted the first patent to test the lesion pattern of a patient without biopsy.

Other examples could be the proposal for protein SLC9A9 (gen Solute carrier family 9) as biomarker for the response to interferon beta, as it happens for serum cytokine profiles The same was proposed to MxA protein mRNA. The presence of anti-MOG, even with CDMS diagnosis, can be considered as a biomarker against MS disease modifying therapies like fingolimod.

Diagnosis of MS has always been made by clinical examination, supported by MRI or CSF tests. According with both the pure autoimmune hypothesis and the immune-mediated hypothesis, researchers expect to find biomarkers able to yield a better diagnosis, and able to predict the response to the different available treatments. As of 2014 no biomarker with perfect correlation has been found, but some of them have shown a special behavior like IgG- and IgM- oligoclonal bands in the cerebrospinal fluid and autoantibodies against neurotropic viruses (MRZ reaction) and the potassium channel Kir4.1.

A biomarker is a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention. Type 0 biomarkers are those related to the course a pathogenic process and type 1 are those that show the effects of the therapeutical intervention.

As of 2014, the only fully specific biomarkers found to date are four proteins in the CSF: CRTAC-IB (cartilage acidic protein), tetranectin (a plasminogen-binding protein), SPARC-like protein (a calcium binding cell signalling glycoprotein), and autotaxin-T (a phosphodiesterase). Nevertheless, abnormal concentrations of non-specific proteins can also help in the diagnosis, like chitinases. This list has been expanded in 2016, with three CSF proteins (Immunoglobulins) reported specific for MS. They are the following immunoglobulins: Ig γ-1 (chain C region), Ig heavy chain V-III (region BRO) and Ig-κ-chain (C region).

Biomarkers are also important for the expected response to therapy. Currently it has been proposed the protein SLC9A9 (gen Solute carrier family 9) as biomarker for the response to interferon beta.

Molecular biomarkers in blood

Blood serum of MS patients shows abnormalities. Endothelin-1 shows maybe the most striking discordance between patients and controls, being a 224% higher in patients than controls.

Creatine and Uric acid levels are lower than normal, at least in women. Ex vivo CD4(+) T cells isolated from the circulation show a wrong TIM-3 (Immunoregulation) behavior, and relapses are associated with CD8(+) T Cells. There is a set of differentially expressed genes between MS and healthy subjects in peripheral blood T cells from clinically active MS patients. There are also differences between acute relapses and complete remissions. Platelets are known to have abnormal high levels.

MS patients are also known to be CD46 defective, and this leads to Interleukin-10 (IL-10) deficiency, being this involved in the inflammatory reactions. Levels of IL-2, IL-10, and GM-CSF are lower in MS females than normal. IL6 is higher instead. These findings do not apply to men. This IL-10 could be related to the mechanism of action of methylprednisolone, together with CCL2. Interleukin IL-12 is also known to be associated with relapses, but this is unlikely to be related to the response to steroids.

Kallikreins are found in serum and are associated with secondary progressive stage. Related to this, it has been found that B1-receptors, part of the kallikrein-kinin-system, are involved in the BBB breakdown.

There is evidence of Apoptosis-related molecules in blood and they are related to disease activity. B cells in CSF appear, and they correlate with early brain inflammation. There is also an overexpression of IgG-free kappa light chain protein in both CIS and RR-MS patients, compared with control subjects, together with an increased expression of an isoforms of apolipoprotein E in RR-MS. Expression of some specific proteins in circulating CD4+ T cells is a risk factor for conversion from CIS to clinically defined multiple sclerosis.

Recently, unique autoantibody patterns that distinguish RRMS, secondary progressive (SPMS), and primary progressive (PPMS) have been found, based on up- and down-regulation of CNS antigens, tested by microarrays. In particular, RRMS is characterized by autoantibodies to heat shock proteins that were not observed in PPMS or SPMS. These antibodies patterns can be used to monitor disease progression.

Finally, a promising biomarker under study is an antibody against the potassium channel protein KIR4.1. This biomarker has been reported to be present in around a half of MS patients, but in nearly none of the controls.

Micro-RNA in blood

Micro-RNA are non-coding RNA of around 22 nucleotides in length. They are present in blood and in CSF. Several studies have found specific micro-RNA signatures for MS. They have been proposed as biomarkers for the presence of the disease and its evolution and some of them like miR-150 are under study, specially for those with lipid-specific oligoclonal IgM bands

Circulating MicroRNAs have been proposed as biomarkers. There is current evidence that at least 60 circulating miRNAs would be dysregulated in MS patient's blood and profiling results are continuously emerging. Circulating miRNAs are highly stable in blood, easy to collect, and the quantification method, if standardized, can be accurate and cheap. They are putative biomarkers to diagnose MS but could also serve differentiating MS subtypes, anticipating relapses and proposing a customized treatment. MiRNA has even been proposed as a primary cause of MS and its white matter damaged areas

Genetic biomarkers for MS type

By RNA profile
Also in blood serum can be found the RNA type of the MS patient. Two types have been proposed classifying the patients as MSA or MSB, allegedly predicting future inflammatory events.
By transcription factor
The autoimmune disease-associated transcription factors EOMES and TBX21 are dysregulated in multiple sclerosis and define a molecular subtype of disease. The importance of this discovery is that the expression of these genes appears in blood and can be measured by a simple blood analysis.
NR1H3 Mutation.
Some PPMS patients have been found to have a special genetic variant named rapidly progressive multiple sclerosis In these cases MS is due to a mutation inside the gene NR1H3, an arginine to glutamine mutation in the position p.Arg415Gln, in an area that codifies the protein LXRA.

In blood vessel tissue

Endothelial dysfunction has been reported in MS and could be used as biomarker via biopsia. Blood circulation is slower in MS patients and can be measured using contrast or by MRI

Interleukin-12p40 has been reported to separate RRMS and CIS from other neurological diseases

In cerebrospinal fluid

The most specific laboratory marker of MS reported to date, as of 2016, is the intrathecal MRZ (Measles, Rubella and Varicella) reaction showing 78% sensitivity and 97% specificity.

It has been known for quite some time that glutamate is present at higher levels in CSF during relapses, maybe because of the IL-17 disregulation, and to MS patients before relapses compared to healthy subjects. This observation has been linked to the activity of the infiltrating leukocytes and activated microglia, and to the damage to the axons and to the oligodendrocytes damage, supposed to be the main cleaning agents for glutamate

Also a specific MS protein has been found in CSF, chromogranin A, possibly related to axonal degeneration. It appears together with clusterin and complement C3, markers of complement-mediated inflammatory reactions. Also Fibroblast growth factor-2 appear higher at CSF.

Varicella-zoster virus particles have been found in CSF of patients during relapses, but this particles are virtually absent during remissions. Plasma Cells in the cerebrospinal fluid of MS patients could also be used for diagnosis, because they have been found to produce myelin-specific antibodies. As of 2011, a recently discovered myelin protein TPPP/p25, has been found in CSF of MS patients

A study found that quantification of several immune cell subsets, both in blood and CSF, showed differences between intrathecal (from the spine) and systemic immunity, and between CSF cell subtypes in the inflammatory and noninflammatory groups (basically RRMS/SPMS compared to PPMS). This showed that some patients diagnosed with PPMS shared an inflammatory profile with RRMS and SPMS, while others didn't.

Other study found using a proteomic analysis of the CSF that the peak intensity of the signals corresponding to Secretogranin II and Protein 7B2 were significantly upregulated in RRMS patients compared to PrMS (p<0.05), whereas the signals of Fibrinogen and Fibrinopeptide A were significantly downregulated in CIS compared to PrMS patients

As of 2014 it is considered that the CSF signature of MS is a combination of cytokines CSF lactate has been found to correlate to disease progression

Three proteins in CSF have been found to be specific for MS. They are the following immunoglobulins: Ig γ-1 (chain C region), Ig heavy chain V-III (region BRO) and Ig-κ-chain (C region)

Other interesting byproduct of the MS attack are the neurofilaments, remainings of the neural damage and the immunoglobulin heavy chains.

Oligoclonal bands

CSF also shows oligoclonal bands (OCB) in the majority (around 95%) of the patients. Several studies have reported differences between patients with and without OCB with regard to clinical parameters such as age, gender, disease duration, clinical severity and several MRI characteristics, together with a varying lesion load. CSF oligoclonal bands can be reflected in serum or not. This points to a heterogeneous origin of them

Though early theories assumed that the OCBs were somehow pathogenic autoantigens, recent research has shown that the immunoglobulins present in them are antibodies against debris, and therefore, OCBs seem to be just a secondary effect of MS.

Given that OCBs are not pathogenic, their remaining importance is to demonstrate the production of intrathecal immunoglobins (IgGs) against debris, but this can be shown by other methods. Specially interesting are the free light chains (FLC), specially the kappa-FLCs (kFLCs). Free kappa chains in CSF have been proposed as a marker for MS evolution

Biomarkers in brain cells and biopsies

Abnormal sodium distribution has been reported in living MS brains. In the early-stage RRMS patients, sodium MRI revealed abnormally high concentrations of sodium in brainstem, cerebellum and temporal pole. In the advanced-stage RRMS patients, abnormally high sodium accumulation was widespread throughout the whole brain, including normal appearing brain tissue. It is currently unknown whether post-mortem brains are consistent with this observation.

The pre-active lesions are clusters of microglia driven by the HspB5 protein, thought to be produced by stressed oligodendrocytes. The presence of HspB5 in biopsies can be a marker for lesion development.

Retinal cells are considered part of the CNS and present a characteristic thickness loss that can separate MS from NMO

Biomarkers for the clinical course

Currently it is possible to distinguish between the three main clinical courses (RRMS, SPMS and PPMS) using a combination of four blood protein tests with an accuracy around 80%

Currently the best predictor for clinical multiple sclerosis is the number of T2 lesions visualized by MRI during the CIS, but it has been proposed to complement it with MRI measures of BBB permeability It is normal to evaluate diagnostic criteria against the "time to conversion to definite".

Imaging biomarkers: MRI, PET and OCT

Magnetic resonance (MRI) and positron emission tomography (PET) are two techniques currently used in MS research. While the first one is routinely used in clinical practice, the second one is also helping to understand the nature of the disease.

In MRI, some post-processing techniques have improved the image. SWI-adjusted magnetic resonance has given results close to 100% specificity and sensitivity respect McDonald's CDMS status and magnetization transfer MRI has shown that NAWM evolves during the disease reducing its magnetization transfer coefficient.

PET is able to show the activation status of microglia, which are macrophage-like cells of the CNS and whose activation is thought to be related to the development of the lesions. Microglial activation is shown using tracers for the 18 kDa translocator protein (TSPO) like the radioligand
PK11195

Biomarkers for MS pathological subtype

Differences have been found between the proteins expressed by patients and healthy subjects, and between attacks and remissions. Using DNA microarray technology groups of molecular biomarkers can be established. For example, it is known that Anti-lipid oligoclonal IgM bands (OCMB) distinguish MS patients with early aggressive course and that these patients show a favourable response to immunomodulatory treatment.

It seems that Fas and MIF are candidate biomarkers of progressive neurodegeneration. Upregulated levels of sFas (soluble form of Fas molecule) were found in MS patients with hypotensive lesions with progressive neurodegeneration, and also levels of MIF appeared to be higher in progressive than in non-progressing patients. Serum TNF-α and CCL2 seem to reflect the presence of inflammatory responses in primary progressive MS.

As previously reported, there is an antibody against the potassium channel protein KIR4.1 which is present in around a half of MS patients, but in nearly none of the controls, pointing towards a heterogeneous etiology in MS. The same happens with B-Cells

DRB3*02:02 patients

Specially interesting is the case of DRB3*02:02 patients (HLA-DRB3*–positive patients), which seem to have a clear autoimmune reaction against a protein called GDP-L-fucose synthase.

Biomarkers for response to therapy

Response to therapy is heterogeneous in MS. Serum cytokine profiles have been proposed as biomarkers for response to Betaseron and the same was proposed to MxA mRNA.

References

  1. ^ Hottenrott, Tilman; Dersch, Rick; Berger, Benjamin; Rauer, Sebastian; Eckenweiler, Matthias; Huzly, Daniela; Stich, Oliver (2015). "The intrathecal, polyspecific antiviral immune response in neurosarcoidosis, acute disseminated encephalomyelitis and autoimmune encephalitis compared to multiple sclerosis in a tertiary hospital cohort". Fluids and Barriers of the CNS. 12: 27. doi:10.1186/s12987-015-0024-8. PMC 4677451. PMID 26652013.
  2. Fabio Duranti; Massimo Pieri; Rossella Zenobi; Diego Centonze; Fabio Buttari; Sergio Bernardini; Mariarita Dessi. "kFLC Index: a novel approach in early diagnosis of Multiple Sclerosis". International Journal of Scientific Research. 4 (8).
  3. Serafeim, Katsavos; Anagnostouli Maria (2013). "Biomarkers in Multiple Sclerosis: An Up-to-Date Overview". Multiple Sclerosis International. 2013: 340508. doi:10.1155/2013/340508. PMC 3564381. PMID 23401777.
  4. Carlson NG, Rose JW (2013). "Vitamin D as a clinical biomarker in multiple sclerosis". Expert Opin Med Diagn (Review). 7 (3): 231–42. doi:10.1517/17530059.2013.772978. PMID 23480560.
  5. Wootla B, Eriguchi M, Rodriguez M (2012). "Is multiple sclerosis an autoimmune disease?". Autoimmune Diseases. 2012: 969657. doi:10.1155/2012/969657. PMC 3361990. PMID 22666554.
  6. Buck Dorothea; Hemmer Bernhard (2014). "Biomarkers of treatment response in multiple sclerosis". Expert Review of Neurotherapeutics. 14 (2): 165–172. doi:10.1586/14737175.2014.874289. PMID 24386967. S2CID 10295564.
  7. Comabella Manuel; Montalban Xavier (2014). "Body fluid biomarkers in multiple sclerosis". The Lancet Neurology. 13 (1): 113–126. doi:10.1016/S1474-4422(13)70233-3. PMID 24331797. S2CID 34302527.
  8. Salehi, Zahra; Doosti, Rozita; Beheshti, Masoumeh; Janzamin, Ehsan; Sahraian, Mohammad Ali; Izad, Maryam (2016). "Differential Frequency of CD8+ T Cell Subsets in Multiple Sclerosis Patients with Various Clinical Patterns". PLOS ONE. 11 (7): e0159565. Bibcode:2016PLoSO..1159565S. doi:10.1371/journal.pone.0159565. PMC 4965085. PMID 27467597.
  9. Lockwood, Sarah Y.; Summers, Suzanne; Eggenberger, Eric; Spence, Dana M. (2016). "An in Vitro Diagnostic for Multiple Sclerosis Based on C-peptide Binding to Erythrocytes". eBioMedicine. 11: 249–252. doi:10.1016/j.ebiom.2016.07.036. PMC 5049924. PMID 27528268.
  10. Lehmann-Werman, Roni; Neiman, Daniel; Zemmour, Hai; Moss, Joshua; Magenheim, Judith; Vaknin-Dembinsky, Adi; Rubertsson, Sten; Nellgård, Bengt; Blennow, Kaj; Zetterberg, Henrik; Spalding, Kirsty; Haller, Michael J.; Wasserfall, Clive H.; Schatz, Desmond A.; Greenbaum, Carla J.; Dorrell, Craig; Grompe, Markus; Zick, Aviad; Hubert, Ayala; Maoz, Myriam; Fendrich, Volker; Bartsch, Detlef K.; Golan, Talia; Ben Sasson, Shmuel A.; Zamir, Gideon; Razin, Aharon; Cedar, Howard; Shapiro, A. M. James; Glaser, Benjamin; et al. (2016). "Identification of tissue-specific cell death using methylation patterns of circulating DNA". Proceedings of the National Academy of Sciences. 113 (13): E1826–E1834. Bibcode:2016PNAS..113E1826L. doi:10.1073/pnas.1519286113. PMC 4822610. PMID 26976580.
  11. Hammack, B. N.; Fung, K. Y.; Hunsucker, S. W.; Duncan, M. W.; Burgoon, M. P.; Owens, G. P.; Gilden, D. H. (Jun 2004). "Proteomic analysis of multiple sclerosis cerebrospinal fluid". Mult Scler. 10 (3): 245–60. doi:10.1191/1352458504ms1023oa. PMID 15222687. S2CID 37117616.
  12. ^ Pavelek Zbysek; et al. (2016). "Proteomic analysis of cerebrospinal fluid for relapsing-remitting multiple sclerosis and clinically isolated syndrome". Biomedical Reports. 5 (1): 35–40. doi:10.3892/br.2016.668. PMC 4906564. PMID 27347402.
  13. Bergman, Joakim; Dring, Ann; Zetterberg, Henrik; Blennow, Kaj; Norgren, Niklas; Gilthorpe, Jonathan; Bergenheim, Tommy; Svenningsson, Anders (2016). "Neurofilament light in CSF and serum is a sensitive marker for axonal white matter injury in MS". Neurology: Neuroimmunology & Neuroinflammation. 3 (5): e271. doi:10.1212/NXI.0000000000000271. PMC 4972001. PMID 27536708.
  14. Klistornera Alexander; et al. (2016). "Diffusivity in multiple sclerosis lesions: At the cutting edge?". NeuroImage: Clinical. 12: 219–226. doi:10.1016/j.nicl.2016.07.003. PMC 4950592. PMID 27489769.
  15. Stephanie Meier; et al. (2023). "Serum Glial Fibrillary Acidic Protein Compared With Neurofilament Light Chain as a Biomarker for Disease Progression in Multiple Sclerosis". JAMA Neurology. 80 (3): 287–297. doi:10.1001/jamaneurol.2022.5250. PMC 10011932. PMID 36745446.
  16. Spadaro Melania; et al. (2016). "Autoantibodies to MOG in a distinct subgroup of adult multiple sclerosis". Neurol Neuroimmunol Neuroinflamm. 3 (5): e257. doi:10.1212/NXI.0000000000000257. PMC 4949775. PMID 27458601.
  17. Happe, LE (Nov 2013). "Choosing the best treatment for multiple sclerosis: comparative effectiveness, safety, and other factors involved in disease-modifying therapy choice". Am J Manag Care. 19 (17 Suppl): S332–42. PMID 24494634.
  18. Finn, Robert (21 May 2015). "Gene Variant Associated with Non-Response to Interferon β". Multiple Sclerosis Discovery Forum. doi:10.7493/msdf.10.18998.1.
  19. ^ Esposito, Federica; Sorosina, Melissa; Ottoboni, Linda; Lim, Elaine T.; Replogle, Joseph M.; Raj, Towfique; Brambilla, Paola; Liberatore, Giuseppe; Guaschino, Clara; Romeo, Marzia; Pertel, Thomas; Stankiewicz, James M.; Martinelli, Vittorio; Rodegher, Mariaemma; Weiner, Howard L.; Brassat, David; Benoist, Christophe; Patsopoulos, Nikolaos A.; Comi, Giancarlo; Elyaman, Wassim; Martinelli Boneschi, Filippo; De Jager, Philip L. (2015). "A pharmacogenetic study implicatesSLC9a9in multiple sclerosis disease activity". Annals of Neurology. 78 (1): 115–127. doi:10.1002/ana.24429. PMID 25914168. S2CID 3210890.
  20. Parnell, GP (Jan 2014). "The autoimmune disease-associated transcription factors EOMES and TBX21 are dysregulated in multiple sclerosis and define a molecular subtype of disease". Clin Immunol. 151 (1): 16–24. doi:10.1016/j.clim.2014.01.003. PMID 24495857.
  21. Hellberg, Sandra; Eklund, Daniel; Gawel, Danuta R.; Köpsén, Mattias; Zhang, Huan; Nestor, Colm E.; Kockum, Ingrid; Olsson, Tomas; Skogh, Thomas; Kastbom, Alf; Sjöwall, Christopher; Vrethem, Magnus; Håkansson, Irene; Benson, Mikael; Jenmalm, Maria C.; Gustafsson, Mika; Ernerudh, Jan (2016). "Dynamic Response Genes in CD4+ T Cells Reveal a Network of Interactive Proteins that Classifies Disease Activity in Multiple Sclerosis". Cell Reports. 16 (11): 2928–2939. doi:10.1016/j.celrep.2016.08.036. PMID 27626663.
  22. Warabi Yoko; et al. (2016). "Spinal cord open-ring enhancement in multiple sclerosis with marked effect of fingolimod". Clinical and Experimental Neuroimmunology. 7 (4): 353–354. doi:10.1111/cen3.12322. S2CID 78240891.
  23. Gross; et al. (2016). "Distinct pattern of lesion distribution in multiple sclerosis is associated with different circulating T-helper and helper-like innate lymphoid cell subsets". Mult Scler. 23 (7): 1025–1030. doi:10.1177/1352458516662726. PMID 27481205. S2CID 3949451.
  24. Johnson, Mark C.; Pierson, Emily R.; Spieker, Andrew J.; Nielsen, A. Scott; Posso, Sylvia; Kita, Mariko; Buckner, Jane H.; Goverman, Joan M. (2016). "Distinct T cell signatures define subsets of patients with multiple sclerosis". Neurology: Neuroimmunology & Neuroinflammation. 3 (5): e278. doi:10.1212/NXI.0000000000000278. PMC 4996538. PMID 27606354.
  25. while others were irresponsive
  26. United States patent US9267945
  27. Hegen, Harald; et al. (2016). "Cytokine profiles show heterogeneity of interferon-β response in multiple sclerosis patients". Neurol Neuroimmunol Neuroinflamm. 3 (2): e202. doi:10.1212/NXI.0000000000000202. PMC 4747480. PMID 26894205.
  28. Matas, Elisabet; Bau, Laura; Martínez-Iniesta, María; Romero-Pinel, Lucía; Mañé-Martínez, M. Alba; Cobo-Calvo, Álvaro; Martínez-Yélamos, Sergio (2016). "MxA mRNA expression as a biomarker of interferon beta response in multiple sclerosis patients". Journal of Neuroimmunology. 291: 73–77. doi:10.1016/j.jneuroim.2015.12.015. PMID 26857498. S2CID 24389171.
  29. Miyazaki, T; Nakajima, H; Motomura, M; Tanaka, K (2016). "A case of recurrent optic neuritis associated with cerebral and spinal cord lesions and autoantibodies against myelin oligodendrocyte glycoprotein relapsed after fingolimod therapy". Clinical Neurology. 56 (4): 265–269. doi:10.5692/clinicalneurol.cn-000756. PMID 27010093.
  30. Wootla B, Eriguchi M, Rodriguez M (2012). "Is multiple sclerosis an autoimmune disease?". Autoimmune Diseases. 2012: 969657. doi:10.1155/2012/969657. PMC 3361990. PMID 22666554.
  31. Buck Dorothea; Hemmer Bernhard (2014). "Biomarkers of treatment response in multiple sclerosis". Expert Review of Neurotherapeutics. 14 (2): 165–172. doi:10.1586/14737175.2014.874289. PMID 24386967. S2CID 10295564.
  32. Comabella Manuel; Montalban Xavier (2014). "Body fluid biomarkers in multiple sclerosis". The Lancet Neurology. 13 (1): 113–126. doi:10.1016/S1474-4422(13)70233-3. PMID 24331797. S2CID 34302527.
  33. Dobson, Ruth; Ramagopalan, Sreeram; Davis, Angharad; Giovannoni, Gavin (August 2013). "Cerebrospinal fluid oligoclonal bands in multiple sclerosis and clinically isolated syndromes: a meta-analysis of prevalence, prognosis and effect of latitude". Journal of Neurology, Neurosurgery, and Psychiatry. 84 (8): 909–914. doi:10.1136/jnnp-2012-304695. ISSN 1468-330X. PMID 23431079. S2CID 19005640.
  34. Villar, L. M.; Masjuan, J.; González-Porqué, P.; Plaza, J.; Sádaba, M. C.; Roldán, E.; Bootello, A.; Alvarez-Cermeño, J. C. (2002-08-27). "Intrathecal IgM synthesis predicts the onset of new relapses and a worse disease course in MS". Neurology. 59 (4): 555–559. doi:10.1212/wnl.59.4.555. ISSN 0028-3878. PMID 12196648. S2CID 41768095.
  35. Brettschneider, Johannes; Tumani, Hayrettin; Kiechle, Ulrike; Muche, Rainer; Richards, Gayle; Lehmensiek, Vera; Ludolph, Albert C.; Otto, Markus (2009-11-05). "IgG antibodies against measles, rubella, and varicella zoster virus predict conversion to multiple sclerosis in clinically isolated syndrome". PLOS ONE. 4 (11): e7638. Bibcode:2009PLoSO...4.7638B. doi:10.1371/journal.pone.0007638. ISSN 1932-6203. PMC 2766627. PMID 19890384.
  36. ^ Srivastava Rajneesh; et al. (2012). "Potassium Channel KIR4.1 as an Immune Target in Multiple Sclerosis". New England Journal of Medicine. 367 (2): 115–123. doi:10.1056/NEJMoa1110740. PMC 5131800. PMID 22784115.
  37. Bielekova, Bibiana; Martin, Roland (2004). "Development of biomarkers in multiple sclerosis". Brain. 127 (7): 1463–1478. doi:10.1093/brain/awh176. PMID 15180926.
  38. Hammack BN, Fung KY, Hunsucker SW, Duncan MW, Burgoon MP, Owens GP, Gilden DH (Jun 2004). "Proteomic analysis of multiple sclerosis cerebrospinal fluid". Mult Scler. 10 (3): 245–60. doi:10.1191/1352458504ms1023oa. PMID 15222687. S2CID 37117616.
  39. Hinsinger G, et al. (2015). "Chitinase 3-like proteins as diagnostic and prognostic biomarkers of multiple sclerosis". Mult Scler. 21 (10): 1251–61. doi:10.1177/1352458514561906. PMID 25698171. S2CID 27290546.
  40. Esposito, Federica; Sorosina, Melissa; Ottoboni, Linda; Lim, Elaine T.; Replogle, Joseph M.; Raj, Towfique; Brambilla, Paola; Liberatore, Giuseppe; Guaschino, Clara; Romeo, Marzia; Pertel, Thomas; Stankiewicz, James M.; Martinelli, Vittorio; Rodegher, Mariaemma; Weiner, Howard L.; Brassat, David; Benoist, Christophe; Patsopoulos, Nikolaos A.; Comi, Giancarlo; Elyaman, Wassim; Martinelli Boneschi, Filippo; De Jager, Philip L. (2015). "A pharmacogenetic study implicatesSLC9a9in multiple sclerosis disease activity". Annals of Neurology. 78 (1): 115–27. doi:10.1002/ana.24429. PMID 25914168. S2CID 3210890.
  41. Haufschild T, Shaw SG, Kesselring J, Flammer J (Mar 2001). "Increased endothelin-1 plasma levels in patients with multiple sclerosis". J Neuroophthalmol. 21 (1): 37–8. doi:10.1097/00041327-200103000-00011. PMID 11315981. S2CID 24772967.
  42. Kanabrocki EL, Ryan MD, Hermida RC, et al. (2008). "Uric acid and renal function in multiple sclerosis". Clin Ter. 159 (1): 35–40. PMID 18399261.
  43. Yang L, Anderson DE, Kuchroo J, Hafler DA (2008). "Lack of TIM-3 Immunoregulation in Multiple Sclerosis". Journal of Immunology. 180 (7): 4409–4414. doi:10.4049/jimmunol.180.7.4409. PMID 18354161.
  44. Malmeström C, Lycke J, Haghighi S, Andersen O, Carlsson L, Wadenvik H, Olsson B (2008). "Relapses in multiple sclerosis are associated with increased CD8(+) T-cell mediated cytotoxicity in CSF". J. Neuroimmunol. 196 (Apr.5): 35–40. doi:10.1016/j.jneuroim.2008.03.001. PMID 18396337. S2CID 206272331.
  45. ^ Satoh J (2008). "". Nippon Rinsho (in Japanese). 66 (6): 1103–11. PMID 18540355.
  46. Sheremata WA, Jy W, Horstman LL, Ahn YS, Alexander JS, Minagar A (2008). "Evidence of platelet activation in multiple sclerosis". J Neuroinflammation. 5 (1): 27. doi:10.1186/1742-2094-5-27. PMC 2474601. PMID 18588683.
  47. Astier AL (2008). "T-cell regulation by CD46 and its relevance in multiple sclerosis". Immunology. 124 (2): 149–54. doi:10.1111/j.1365-2567.2008.02821.x. PMC 2566619. PMID 18384356.
  48. Kanabrocki EL, Ryan MD, Lathers D, Achille N, Young MR, Cauteren JV, Foley S, Johnson MC, Friedman NC, Siegel G, Nemchausky BA (2007). "Circadian distribution of serum cytokines in multiple sclerosis". Clin. Ter. 158 (2): 157–62. PMID 17566518.
  49. Rentzos M, Nikolaou C, Rombos A, Evangelopoulos ME, Kararizou E, Koutsis G, Zoga M, Dimitrakopoulos A, Tsoutsou A, Sfangos C (2008). "Effect of treatment with methylprednisolone on the serum levels of IL-12, IL-10 and CCL2 chemokine in patients with multiple sclerosis in relapse". Clinical Neurology and Neurosurgery. 110 (10): 992–6. doi:10.1016/j.clineuro.2008.06.005. PMID 18657352. S2CID 2630371.
  50. Scarisbrick IA, Linbo R, Vandell AG, Keegan M, Blaber SI, Blaber M, Sneve D, Lucchinetti CF, Rodriguez M, Diamandis EP (2008). "Kallikreins are associated with secondary progressive multiple sclerosis and promote neurodegeneration". Biological Chemistry. 389 (6): 739–45. doi:10.1515/BC.2008.085. PMC 2580060. PMID 18627300.
  51. New Control System Of The Body Discovered - Important Modulator Of Immune Cell Entry Into The Brain - Perhaps New Target For The Therapy, Dr. Ulf Schulze-Topphoff, Prof. Orhan Aktas, and Professor Frauke Zipp (Cecilie Vogt-Clinic, Charité - Universitätsmedizin Berlin, Max Delbrück Center for Molecular Medicine (MDC) Berlin-Buch and NeuroCure Research Center)
  52. Schulze-Topphoff U, Prat A, Prozorovski T, et al. (July 2009). "Activation of kinin receptor B1 limits encephalitogenic T lymphocyte recruitment to the central nervous system". Nat. Med. 15 (7): 788–93. doi:10.1038/nm.1980. PMC 4903020. PMID 19561616.
  53. Rinta S, Kuusisto H, Raunio M, et al. (October 2008). "Apoptosis-related molecules in blood in multiple sclerosis". J. Neuroimmunol. 205 (1–2): 135–41. doi:10.1016/j.jneuroim.2008.09.002. PMID 18963025. S2CID 28592242.
  54. Kuenz B, Lutterotti A, Ehling R, et al. (2008). Zimmer J (ed.). "Cerebrospinal Fluid B Cells Correlate with Early Brain Inflammation in Multiple Sclerosis". PLOS ONE. 3 (7): e2559. Bibcode:2008PLoSO...3.2559K. doi:10.1371/journal.pone.0002559. PMC 2438478. PMID 18596942. Open access icon
  55. Chiasserini D, Di Filippo M, Candeliere A, Susta F, Orvietani PL, Calabresi P, Binaglia L, Sarchielli P (2008). "CSF proteome analysis in multiple sclerosis patients by two-dimensional electrophoresis". European Journal of Neurology. 15 (9): 998–1001. doi:10.1111/j.1468-1331.2008.02239.x. PMID 18637954. S2CID 27735092.
  56. Frisullo G, Nociti V, Iorio R, et al. (October 2008). "The persistency of high levels of pSTAT3 expression in circulating CD4+ T cells from CIS patients favors the early conversion to clinically defined multiple sclerosis". J. Neuroimmunol. 205 (1–2): 126–34. doi:10.1016/j.jneuroim.2008.09.003. PMID 18926576. S2CID 27303451.
  57. Proceedings of the National Academy of sciences, complementary information
  58. Quintana FJ, Farez MF, Viglietta V, et al. (December 2008). "Antigen microarrays identify unique serum autoantibody signatures in clinical and pathologic subtypes of multiple sclerosis". Proc. Natl. Acad. Sci. U.S.A. 105 (48): 18889–94. Bibcode:2008PNAS..10518889Q. doi:10.1073/pnas.0806310105. PMC 2596207. PMID 19028871.
  59. Villar LM, Masterman T, Casanova B, et al. (June 2009). "CSF oligoclonal band patterns reveal disease heterogeneity in multiple sclerosis". J. Neuroimmunol. 211 (1–2): 101–4. doi:10.1016/j.jneuroim.2009.03.003. PMID 19443047. S2CID 31814258.
  60. Martin Nellie A., Illes Zsolt (2014). "Differentially expressed microRNA in multiple sclerosis: A window into pathogenesis?". Clinical and Experimental Neuroimmunology. 5 (2): 149–161. doi:10.1111/cen3.12131. S2CID 84623296.
  61. Gandhi R, Healy B, Gholipour T, Egorova S, Musallam A, Hussain MS, Nejad P, Patel B, Hei H, Khoury S, Quintana F, Kivisakk P, Chitnis T, Weiner HL (Jun 2013). "Circulating microRNAs as biomarkers for disease staging in multiple sclerosis". Ann Neurol. 73 (6): 729–40. doi:10.1002/ana.23880. PMID 23494648. S2CID 205344499.
  62. Bergman P, Piket E, Khademi M, James T, Brundin L, Olsson T, Piehl F, Jagodic M (2016). "Circulating miR-150 in CSF is a novel candidate biomarker for multiple sclerosis". Neurol Neuroimmunol Neuroinflamm. 3 (3): e219. doi:10.1212/NXI.0000000000000219. PMC 4841644. PMID 27144214.
  63. Quintana E, et al. (2017). "miRNAs in cerebrospinal fluid identify patients with MS and specifically those with lipid-specific oligoclonal IgM bands". Multiple Sclerosis Journal. 23 (13): 1716–1726. doi:10.1177/1352458516684213. PMID 28067602. S2CID 23439273.
  64. Jagot, Ferdinand; Davoust, Nathalie (2016). "Is It worth Considering Circulating microRNAs in Multiple Sclerosis?". Frontiers in Immunology. 7: 129. doi:10.3389/fimmu.2016.00129. PMC 4821089. PMID 27092141.
  65. Huang, Qingrong; Xiao, Bo; Ma, Xinting; Qu, Mingjuan; Li, Yanmin; Nagarkatti, Prakash; Nagarkatti, Mitzi; Zhou, Juhua (2016). "MicroRNAs associated with the pathogenesis of multiple sclerosis". Journal of Neuroimmunology. 295–296: 148–161. doi:10.1016/j.jneuroim.2016.04.014. PMID 27235360. S2CID 37072576.
  66. Ottoboni L, Keenan BT, Tamayo P, Kuchroo M, Mesirov JP, Buckle GJ, Khoury SJ, Hafler DA, Weiner HL, De Jager PL (2012). "An RNA Profile Identifies Two Subsets of Multiple Sclerosis Patients Differing in Disease Activity". Sci Transl Med. 4 (153): 153ra131. doi:10.1126/scitranslmed.3004186. PMC 3753678. PMID 23019656.
  67. Parnell GP, Gatt PN, Krupa M, Nickles D, McKay FC, Schibeci SD, Batten M, Baranzini S, Henderson A, Barnett M, Slee M, Vucic S, Stewart GJ, Booth DR, et al. (2014). "The autoimmune disease-associated transcription factors EOMES and TBX21 are dysregulated in multiple sclerosis and define a molecular subtype of disease". Clinical Immunology. 151 (1): 16–24. doi:10.1016/j.clim.2014.01.003. PMID 24495857.
  68. Wang Zhe; et al. (2016). "Nuclear Receptor NR1H3 in Familial Multiple Sclerosis". Neuron. 90 (5): 948–954. doi:10.1016/j.neuron.2016.04.039. PMC 5092154. PMID 27253448.
  69. Plumb J, McQuaid S, Mirakhur M, Kirk J (April 2002). "Abnormal endothelial tight junctions in active lesions and normal-appearing white matter in multiple sclerosis". Brain Pathol. 12 (2): 154–69. doi:10.1111/j.1750-3639.2002.tb00430.x. PMC 8095734. PMID 11958369.
  70. Mancini, M, Cerebral circulation time in the evaluation of neurological diseases
  71. Meng Law et al. Microvascular Abnormality in Relapsing-Remitting Multiple Sclerosis: Perfusion MR Imaging Findings in Normal-appearing White Matter
  72. Orbach, Rotem; Gurevich, Michael; Achiron, Anat (2014). "Interleukin-12p40 in the spinal fluid as a biomarker for clinically isolated syndrome". Multiple Sclerosis Journal. 20 (1): 35–42. doi:10.1177/1352458513491166. PMID 23722323. S2CID 23277386.
  73. Jarius S, Eichhorn P, Franciotta D, et al. (2016). "The MRZ reaction as a highly specific marker of multiple sclerosis: re-evaluation and structured review of the literature". J. Neurol. 264 (3): 453–466. doi:10.1007/s00415-016-8360-4. PMID 28005176. S2CID 25322362.
  74. Sarchielli P, Greco L, Floridi A, Floridi A, Gallai V (2003). "Excitatory amino acids and multiple sclerosis: evidence from cerebrospinal fluid". Arch. Neurol. 60 (8): 1082–8. doi:10.1001/archneur.60.8.1082. PMID 12925363.
  75. Kostic Milos; et al. (2017). "IL-17 signalling in astrocytes promotes glutamate excitotoxicity: Indications for the link between inflammatory and neurodegenerative events in multiple sclerosis". Multiple Sclerosis and Related Disorders. 11: 12–17. doi:10.1016/j.msard.2016.11.006. PMID 28104249.
  76. Frigo M, Cogo MG, Fusco ML, Gardinetti M, Frigeni B (2012). "Glutamate and multiple sclerosis". Curr. Med. Chem. 19 (9): 1295–9. doi:10.2174/092986712799462559. PMID 22304707.
  77. Pitt David; et al. (2003). "Glutamate uptake by oligodendrocytes". Neurology. 61 (8): 1113–1120. doi:10.1212/01.WNL.0000090564.88719.37. PMID 14581674. S2CID 42422422.
  78. Stoop MP, Dekker LJ, Titulaer MK, et al. (2008). "Multiple sclerosis-related proteins identified in cerebrospinal fluid by advanced mass spectrometry". Proteomics. 8 (8): 1576–85. doi:10.1002/pmic.200700446. PMID 18351689. S2CID 41766020.
  79. Sarchielli P, Di Filippo M, Ercolani MV, et al. (April 2008). "Fibroblast growth factor-2 levels are elevated in the cerebrospinal fluid of multiple sclerosis patients". Neurosci. Lett. 435 (3): 223–8. doi:10.1016/j.neulet.2008.02.040. PMID 18353554. S2CID 26004791.
  80. Sotelo J, Martínez-Palomo A, Ordoñez G, Pineda B (2008). "Varicella-zoster virus in cerebrospinal fluid at relapses of multiple sclerosis". Annals of Neurology. 63 (3): 303–11. doi:10.1002/ana.21316. PMID 18306233. S2CID 36489072.
  81. von Büdingen HC, Harrer MD, Kuenzle S, Meier M, Goebels N (July 2008). "Clonally expanded plasma cells in the cerebrospinal fluid of MS patients produce myelin-specific antibodies". European Journal of Immunology. 38 (7): 2014–23. doi:10.1002/eji.200737784. PMID 18521957.
  82. Vincze O, Oláh J, Zádori D, Klivényi P, Vécsei L, Ovádi J (May 2011). "A new myelin protein, TPPP/p25, reduced in demyelinated lesions is enriched in cerebrospinal fluid of multiple sclerosis". Biochem. Biophys. Res. Commun. 409 (1): 137–41. doi:10.1016/j.bbrc.2011.04.130. PMID 21565174.
  83. Han, S.; Lin, Y. C.; Wu, T.; Salgado, A. D.; Mexhitaj, I.; Wuest, S. C.; Romm, E.; Ohayon, J.; Goldbach-Mansky, R.; Vanderver, A.; Marques, A.; Toro, C.; Williamson, P.; Cortese, I.; Bielekova, B. (2014). "Comprehensive Immunophenotyping of Cerebrospinal Fluid Cells in Patients with Neuroimmunological Diseases". The Journal of Immunology. 192 (6): 2551–63. doi:10.4049/jimmunol.1302884. PMC 4045479. PMID 24510966.
  84. Liguori, Maria; Qualtieri, Antonio; Tortorella, Carla; Direnzo, Vita; Bagalà, Angelo; Mastrapasqua, Mariangela; Spadafora, Patrizia; Trojano, Maria (2014). "Proteomic Profiling in Multiple Sclerosis Clinical Courses Reveals Potential Biomarkers of Neurodegeneration". PLOS ONE. 9 (8): e103984. Bibcode:2014PLoSO...9j3984L. doi:10.1371/journal.pone.0103984. PMC 4123901. PMID 25098164.
  85. Burman; et al. (Oct 2014). "The cerebrospinal fluid cytokine signature of multiple sclerosis: A homogenous response that does not conform to the Th1/Th2/Th17 convention". J Neuroimmunol. 277 (1–2): 153–159. doi:10.1016/j.jneuroim.2014.10.005. PMID 25457841. S2CID 206277371.
  86. Albanese Maria; et al. (2016). "Cerebrospinal fluid lactate is associated with multiple sclerosis disease progression". Journal of Neuroinflammation. 13: 36. doi:10.1186/s12974-016-0502-1. PMC 4750170. PMID 26863878.
  87. Soelberg Sorensen Per, Sellebjerg Finn (2016). "Neurofilament in CSF—A biomarker of disease activity and long-term prognosis in multiple sclerosis". Mult Scler. 22 (9): 1112–3. doi:10.1177/1352458516658560. PMID 27364323.
  88. Delgado-García; et al. (2014). "A new risk variant for multiple sclerosis at the immunoglobulin heavy chain locus associates with intrathecal IgG, IgM index and oligoclonal bands". Mult Scler. 21 (9): 1104–1111. doi:10.1177/1352458514556302. hdl:10261/133187. PMID 25392328. S2CID 206701193.
  89. Huttner HB, Schellinger PD, Struffert T, et al. (July 2009). "MRI criteria in MS patients with negative and positive oligoclonal bands: equal fulfillment of Barkhof's criteria but different lesion patterns". J. Neurol. 256 (7): 1121–5. doi:10.1007/s00415-009-5081-y. PMID 19252765. S2CID 25553346.
  90. Villar, Luisa M.; Masterman, Thomas; Casanova, Bonaventura; Gómez-Rial, José; Espiño, Mercedes; Sádaba, María C.; González-Porqué, Pedro; Coret, Francisco; Álvarez-Cermeño, José C. (2009). "CSF oligoclonal band patterns reveal disease heterogeneity in multiple sclerosis". Journal of Neuroimmunology. 211 (1–2): 101–4. doi:10.1016/j.jneuroim.2009.03.003. PMID 19443047. S2CID 31814258.
  91. Wingera RC, Zamvil SS (2016). "Antibodies in multiple sclerosis oligoclonal bands target debris". Proceedings of the National Academy of Sciences of the United States of America. 113 (28): 7696–8. Bibcode:2016PNAS..113.7696W. doi:10.1073/pnas.1609246113. PMC 4948325. PMID 27357674.
  92. Villar LM, Espiño M, Costa-Frossard L, Muriel A, Jiménez J, Alvarez-Cermeño JC (November 2012). "High levels of cerebrospinal fluid free kappa chains predict conversion to multiple sclerosis". Clin. Chim. Acta. 413 (23–24): 1813–6. doi:10.1016/j.cca.2012.07.007. PMID 22814197.
  93. Zaaraoui, Wafaa; Konstandin, Simon; Audoin, Bertrand; Nagel, Armin M.; Rico, Audrey; Malikova, Irina; Soulier, Elisabeth; Viout, Patrick; Confort-Gouny, Sylviane; Cozzone, Patrick J.; Pelletier, Jean; Schad, Lothar R.; Ranjeva, Jean-Philippe (2012). "Distribution of Brain Sodium Accumulation Correlates with Disability in Multiple Sclerosis: A Cross-sectional23Na MR Imaging Study". Radiology. 264 (3): 859–867. doi:10.1148/radiol.12112680. PMID 22807483.
  94. Bsibsi M, Holtman IR, Gerritsen WH, Eggen BJ, Boddeke E, van der Valk P, van Noort JM, Amor S (2013). "Alpha-B-Crystallin Induces an Immune-Regulatory and Antiviral Microglial Response in Preactive Multiple Sclerosis Lesions". J Neuropathol Exp Neurol. 72 (10): 970–9. doi:10.1097/NEN.0b013e3182a776bf. PMID 24042199.
  95. Manogaran, Praveena; Traboulsee, Anthony L.; Lange, Alex P. (2016). "Longitudinal Study of Retinal Nerve Fiber Layer Thickness and Macular Volume in Patients with Neuromyelitis Optica Spectrum Disorder". Journal of Neuro-Ophthalmology. 36 (4): 363–368. doi:10.1097/WNO.0000000000000404. PMID 27416520. S2CID 22865622.
  96. Tejera-Alhambra, Marta; Casrouge, Armanda; De Andrés, Clara; Seyfferth, Ansgar; Ramos-Medina, Rocío; Alonso, Bárbara; Vega, Janet; Fernández-Paredes, Lidia; Albert, Matthew L.; Sánchez-Ramón, Silvia (2015). "Plasma Biomarkers Discriminate Clinical Forms of Multiple Sclerosis". PLOS ONE. 10 (6): e0128952. Bibcode:2015PLoSO..1028952T. doi:10.1371/journal.pone.0128952. PMC 4454618. PMID 26039252.
  97. Cramer SP, Modvig S, Simonsen HJ, Frederiksen JL, Larsson HB (Jul 2015). "Permeability of the blood-brain barrier predicts conversion from optic neuritis to multiple sclerosis". Brain. 138 (Pt 9): 2571–83. doi:10.1093/brain/awv203. PMC 4547053. PMID 26187333.
  98. Beggs CB, Shepherd SJ, Dwyer MG, Polak P, Magnano C, Carl E, Poloni GU, Weinstock-Guttman B, Zivadinov R (Oct 2012). "Sensitivity and specificity of SWI venography for detection of cerebral venous alterations in multiple sclerosis". Neurol Res. 34 (8): 793–801. doi:10.1179/1743132812Y.0000000048. PMID 22709857. S2CID 10318031.
  99. Laule C, et al. (Aug 2003). "Evolution of focal and diffuse magnetisation transfer abnormalities in multiple sclerosis". J Neurol. 250 (8): 924–31. doi:10.1007/s00415-003-1115-z. PMID 12928910. S2CID 13407228.
  100. Park, Eunkyung; Gallezot, Jean-Dominique; Delgadillo, Aracely; Liu, Shuang; Planeta, Beata; Lin, Shu-Fei; o'Connor, Kevin C.; Lim, Keunpoong; Lee, Jae-Yun; Chastre, Anne; Chen, Ming-Kai; Seneca, Nicholas; Leppert, David; Huang, Yiyun; Carson, Richard E.; Pelletier, Daniel (2015). "11C-PBR28 imaging in multiple sclerosis patients and healthy controls: Test-retest reproducibility and focal visualization of active white matter areas". European Journal of Nuclear Medicine and Molecular Imaging. 42 (7): 1081–1092. doi:10.1007/s00259-015-3043-4. PMID 25833352. S2CID 21974135.
  101. Airas, L.; Rissanen, E.; Rinne, J. (2016). "Imaging of microglial activation in MS using PET: Research use and potential future clinical application". Mult. Scler. 23 (Oct 19): 496–504. doi:10.1177/1352458516674568. PMID 27760860.
  102. Herranz, Elena; Giannì, Costanza; Louapre, Céline; Treaba, Constantina A.; Govindarajan, Sindhuja T.; Ouellette, Russell; Loggia, Marco L; Sloane, Jacob A.; Madigan, Nancy; Izquierdo-Garcia, David; Ward, Noreen; Mangeat, Gabriel; Granberg, Tobias; Klawiter, Eric C.; Catana, Ciprian; Hooker, Jacob M; Taylor, Norman; Ionete, Carolina; Kinkel, Revere P.; Mainero, Caterina (2016). "The neuroinflammatory component of gray matter pathology in multiple sclerosis". Annals of Neurology. 80 (5): 776–790. doi:10.1002/ana.24791. PMC 5115951. PMID 27686563.
  103. Venneti, Sriram; Lopresti, Brian; Wiley, Clayton (2013). "Molecular imaging of microglia/macrophages in the brain". Glia. 61 (1): 10–23. doi:10.1002/glia.22357. PMC 3580157. PMID 22615180.
  104. García-Barragán N, Villar LM, Espiño M, Sádaba MC, González-Porqué P, Alvarez-Cermeño JC (March 2009). "Multiple sclerosis patients with anti-lipid oligoclonal IgM show early favourable response to immunomodulatory treatment". Eur. J. Neurol. 16 (3): 380–5. doi:10.1111/j.1468-1331.2008.02504.x. PMID 19175382. S2CID 33302394.
  105. Hagman S, Raunio M, Rossi M, Dastidar P, Elovaara I (May 2011). "Disease-associated inflammatory biomarker profiles in blood in different subtypes of multiple sclerosis: Prospective clinical and MRI follow-up study". Journal of Neuroimmunology. 234 (1–2): 141–7. doi:10.1016/j.jneuroim.2011.02.009. PMID 21397339. S2CID 45101259.
  106. Kuerten, Stefanie; Pommerschein, Giovanna; Barth, Stefanie K.; Hohmann, Christopher; Milles, Bianca; Sammer, Fabian W.; Duffy, Cathrina E.; Wunsch, Marie; Rovituso, Damiano M.; Schroeter, Michael; Addicks, Klaus; Kaiser, Claudia C.; Lehmann, Paul V. (2014). "Identification of a B cell-dependent subpopulation of multiple sclerosis by measurements of brain-reactive B cells in the blood". Clinical Immunology. 152 (1–2): 20–4. doi:10.1016/j.clim.2014.02.014. PMID 24607792.
  107. University of Zurich (2018, October 11). Link Between Gut Flora and Multiple Sclerosis Discovered. NeuroscienceNews. Retrieved October 11, 2018
  108. Planas, Raquel; Santos, Radleigh; Tomas-Ojer, Paula; Cruciani, Carolina; Lutterotti, Andreas; Faigle, Wolfgang; Schaeren-Wiemers, Nicole; Espejo, Carmen; Eixarch, Herena; Pinilla, Clemencia; Martin, Roland; Sospedra, Mireia (2018). "GDP-l-fucose synthase is a CD4+ T cell–specific autoantigen in DRB3*02:02 patients with multiple sclerosis" (PDF). Science Translational Medicine. 10 (462): eaat4301. doi:10.1126/scitranslmed.aat4301. PMID 30305453.
  109. Hegen Harald; et al. (2016). "Cytokine profiles show heterogeneity of interferon-β response in multiple sclerosis patients". Neurol Neuroimmunol Neuroinflamm. 3 (2): e202. doi:10.1212/NXI.0000000000000202. PMC 4747480. PMID 26894205.
  110. Matas, Elisabet; Bau, Laura; Martínez-Iniesta, María; Romero-Pinel, Lucía; Mañé-Martínez, M. Alba; Cobo-Calvo, Álvaro; Martínez-Yélamos, Sergio (2016). "MxA mRNA expression as a biomarker of interferon beta response in multiple sclerosis patients". Journal of Neuroimmunology. 291: 73–7. doi:10.1016/j.jneuroim.2015.12.015. PMID 26857498. S2CID 24389171.
Categories: