Wednesday, 15 May 2013

Biomarkers for predicting MS type

It is not the age of aquarius, but the age of tautologies. #MSBlog #MSResearch

"This study shows that when you analyse immune markers in the cerebrospinal fluid of MSers you can tell apart MSers with RRMS and PPMS and NMOers. I am not surprised by this. RRMS and PPMS are clinically different and NMO is a different disease. Will this be helpful clinically? Unlikely unless the information can be used to select a specific therapy or predict a response to a specific treatment."

"These types of studies are intrinsically flawed; they are an example of a tautology. A tautology is when you define a disease using a set of criteria, for example the McDonald criteria for MS or the Mayo Clinic criteria for NMO. You then use these criteria to define sets of diseased people and when you find differences between the sets, or groups, you use the differences to justify the validity of the diagnostic criteria. What needs to be done is you need to use a non-hypothesis driven algorithm to analyse the data to see if can define distinct groups based on a biomarker signature. If these groups then correspond to the clinical groupings you have real biology to support the clinical disease classifications. I am really tired of tautologous or circular science; I used to practice it myself."

Matsushita et al. Characteristic cerebrospinal fluid cytokine/chemokine profiles in neuromyelitis optica, relapsing remitting or primary progressive multiple sclerosis.PLoS One. 2013 Apr 18;8(4):e61835. doi: 10.1371/journal.pone.0061835. Print 2013.

BACKGROUND: Differences in cytokine/chemokine profiles among patients with neuromyelitis optica (NMO), relapsing remitting multiple sclerosis (RRMS), and primary progressive MS (PPMS), and the relationships of these profiles with clinical and neuroimaging features are unclear. A greater understanding of these profiles may help in differential diagnosis.

METHODS & PRINCIPAL FINDINGS: We measured 27 cytokines/chemokines and growth factors in CSF collected from 20 patients with NMO, 26 with RRMS, nine with PPMS, and 18 with other non-inflammatory neurological diseases (OND) by multiplexed fluorescent bead-based immunoassay. Interleukin (IL)-17A, IL-6, CXCL8 and CXCL10 levels were significantly higher in NMO patients than in OND and RRMS patients at relapse, while granulocyte-colony stimulating factor (G-CSF) and CCL4 levels were significantly higher in NMO patients than in OND patients. In NMO patients, IL-6 and CXCL8 levels were positively correlated with disability and CSF protein concentration while IL-6, CXCL8, G-CSF, granulocyte-macrophage colony-stimulating factor (GM-CSF) and IFN-γ were positively correlated with CSF neutrophil counts at the time of sample collection. In RRMS patients, IL-6 levels were significantly higher than in OND patients at the relapse phase while CSF cell counts were negatively correlated with the levels of CCL2. Correlation coefficients of cytokines/chemokines in the relapse phase were significantly different in three combinations, IL-6 and GM-CSF, G-CSF and GM-CSF, and GM-CSF and IFN-γ, between RRMS and NMO/NMOSD patients. In PPMS patients, CCL4 and CXCL10 levels were significantly higher than in OND patients.

CONCLUSIONS: Our findings suggest distinct cytokine/chemokine alterations in CSF exist among NMO, RRMS and PPMS. In NMO, over-expression of a cluster of Th17- and Th1-related proinflammatory cytokines/chemokines is characteristic, while in PPMS, increased CCL4 and CXCL10 levels may reflect on-going low grade T cell and macrophage/microglia inflammation in the central nervous system. In RRMS, only a mild elevation of proinflammatory cytokines/chemokines was detectable at relapse.

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