![]() ![]() Despite overlap in clinical measures in these three diseases, the distinct plasma metabolic patterns support their distinct serological profiles and confirm that these conditions are indeed different at a molecular level. RRMS plasma exhibited increased histidine and glucose, along with decreased lactate, alanine, and large high density lipoproteins while MOG-Ab disease plasma was defined by increases in formate and leucine coupled with decreased myo-inositol. The AQP4-Ab NMOSD metabolic profile was characterised by decreased levels of scyllo-inositol and small high density lipoprotein particles along with an increase in large low density lipoprotein particles relative to both RRMS and MOG-Ab disease. In addition, OPLS-DA identified AQP4-Ab NMOSD patient samples with low/undetectable antibody levels with an accuracy of 92%. In all instances, the models were highly discriminatory, with a distinct metabolite pattern identified for each disease. Orthogonal partial-least squares discriminatory analysis (OPLS-DA) was used to identify significant differences in the plasma metabolite concentrations and produce models (mathematical algorithms) capable of identifying these diseases. Plasma samples from 108 patients (34 RRMS, 54 AQP4-Ab NMOSD, and 20 MOG-Ab disease) were analysed by nuclear magnetic resonance spectroscopy followed by lipoprotein profiling. ![]() ![]() We sought to discover whether plasma metabolic profiling could provide biomarkers of these three diseases and explore if the metabolic differences are independent of antibody titre. Therefore, there is still a need to discover antibody-independent biomarkers. However, antibody levels are not prognostic and may become undetectable after treatment or during remission. The overlapping clinical features of relapsing remitting multiple sclerosis (RRMS), aquaporin-4 (AQP4)-antibody (Ab) neuromyelitis optica spectrum disorder (NMOSD), and myelin oligodendrocyte glycoprotein (MOG)-Ab disease mean that detection of disease specific serum antibodies is the gold standard in diagnostics. ![]()
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