Beneficial changes in the gut microbiome of patients with multiple sclerosis after consumption of Neu-REFIX B-glucan in a clinical trial
Dedeepiya, V. D.; Vetrievel, C.; Ikewaki, N.; Yamamoto, N.; Kawashima, H.; Ichiyama, K.; Senthilkumar, R.; Preethy, S.; Abraham, S. J.
Show abstract
BackgroundMultiple sclerosis (MS) is a debilitating demyelinating disease and recent evidences are giving cues towards correlation of disease severity to gut microbiome dysbiosis. However, there havent been any reported interventions that beneficially modifies the gut microbiome to yield a clinically discernible improvement. Having earlier reported the clinical effects of a biological response modifier beta-glucan (BRMG) produced by the N-163 strain of Aureobasidum pullulans, commercially available as Neu-REFIX, which decreased the biomarkers of inflammation and produced beneficial immune-modulation in twelve MS patients in 60 days, we evaluated their gut microbiome in the present study. MethodsTwelve patients diagnosed with MS participated in the study. Each consumed 16 g gel of the NEU-REFIX beta-Glucan for 60 days. Whole genome metagenomic sequencing was performed on the fecal samples before and after Neu-REFIX intervention. ResultsPost-intervention analysis showed that Actinobacteria followed by Bacteroides was the major family. Abundance of beneficial genera such as Bifidobacterium, Collinsela, Prevotella, Lactobacillus and species such as Prevotella copri (p-value=0.4), Bifidobacterium longum (p-value=0.2), Faecalibacterium prausnitzii (p-value=0.06), Siphoviridae (p-value=0.06) increased while inflammation associated genera such as Blautia (p-value=0.06), Ruminococcus (p-value=0.007) and Dorea (p-value = 0.03) decreased in abundance. ConclusionRestoration of gut eubiosis in terms of both increase in abundance of the good microbiome and suppression of the harmful ones which also correlate with earlier reported clinical improvement in MS patients makes this Neu-REFIX beta-glucan, a potential disease modifying therapy (DMT) requiring larger studies for validation in MS and other auto-immune-inflammatory conditions where a safe intervention for immune modulation is vital.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Olfactory Dysfunction in Patients with Multiple Sclerosis; A Systematic Review and Meta-Analysis 95%
- BMSCs differentiated into neurons, astrocytes and oligodendrocytesalleviatedthe inflammation and demyelination of EAE mice models 94%
- A prospective, observational study on conversion of Clinically Isolated Syndrome to Multiple Sclerosis during 4-year period (MS NEO study) in Taiwan 93%
Similar papers in this journal
- Biliary microbiota and bile acids composition in cholelithiasis 92%
- Expression of nitric oxide synthase and nitric oxide levels in peripheral blood cells and oxidized low-density lipoprotein levels in saliva as early markers of severe dengue 92%
- Design of multi epitope-based peptide vaccine against E protein of human COVID-19: An immunoinformatics approach 90%
Similar papers in this journal
- Fear of relapse and quality of life in multiple sclerosis: the mediating role of psychological resilience 92%
- Rituximab in the treatment of multiple sclerosis in the Hospital District of Southwest Finland 91%
- Humoral and cellular immune responses to SARS CoV-2 vaccination in Persons with Multiple Sclerosis and NMOSD patients receiving immunomodulatory treatments 90%
Similar papers in this journal
Similar papers in this journal
- Lupus Gut Microbiota Transplants Cause Autoimmunity and Inflammation 93%
- Metagenome-wide association study of gut microbiome features for myositis 92%
- Fecal immunoglobulin A (IgA) and its subclasses in systemic lupus erythematosus patients are nuclear antigen reactive and this feature correlates with gut permeability marker levels 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.