Lymphocyte Subsets Are Associated with Disease Status in Neuromyelitis Optica Spectrum Disorders

Predictive role of blood-based indicators in neuromyelitis optica spectrum disorders

Introduction

This study aimed to assess the predictive role of blood markers in neuromyelitis optica spectrum disorders (NMOSD).

Methods

Data from patients with NMOSD, multiple sclerosis (MS), and healthy individuals were retrospectively collected in a 1:1:1 ratio. The expanded disability status scale (EDSS) score was used to assess the severity of the NMOSD upon admission. Receiver operating characteristic (ROC) curve analysis was used to distinguish NMOSD patients from healthy individuals, and active NMOSD from remitting NMOSD patients. Binary logistic regression analysis was used to evaluate risk factors that could be used to predict disease recurrence. Finally, Wilcoxon signed-rank test or matched-sample t-test was used to analyze the differences between the indicators in the remission and active phases in the same NMOSD patient.

Results

Among the 54 NMOSD patients, neutrophil count, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) (platelet × NLR) were significantly higher than those of MS patients and healthy individuals and positively correlated with the EDSS score of NMOSD patients at admission. PLR can be used to simultaneously distinguish between NMOSD patients in the active and remission phase. Eleven (20.4%) of the 54 patients had recurrence within 12 months. We found that monocyte-to-lymphocyte ratio (MLR) (AUC = 0.76, cut-off value = 0.34) could effectively predict NMOSD recurrence. Binary logistic regression analysis showed that a higher MLR at first admission was the only risk factor for recurrence (p = 0.027; OR = 1.173; 95% CI = 1.018-1.351). In patients in the relapsing phase, no significant changes in monocyte and lymphocyte count was observed from the first admission, whereas patients in remission had significantly higher levels than when they were first admitted.

Conclusion

High PLR is a characteristic marker of active NMOSD, while high MLR is a risk factor for disease recurrence. These inexpensive indicators should be widely used in the diagnosis, prognosis, and judgment of treatment efficacy in NMOSD.

Relationship between gut microbiota and lymphocyte subsets in Chinese Han patients with spinal cord injury

This study is to investigate the changes of lymphocyte subsets and the gut microbiota in Chinese Han patients with spinal cord injury (SCI). We enrolled 23 patients with SCI and 21 healthy controls. Blood and fecal samples were collected. The proportion of lymphocyte subsets was detected by flow cytometry. 16S rDNA sequencing of the V4 region was used to analyze the gut microbiota. The changes of the gut microbiota were analyzed by bioinformatics. Correlation analysis between gut microbiota and lymphocyte subsets was performed. CD4 + cells, CD4 + /CD8 + ratio and CD4 + CD8 + cells in peripheral blood of SCI patients were significantly lower than those of the control group (P < 0.05). There was no significant difference in B cells and CIK cells between the SCI group and the control group. The gut microbiota community diversity index of SCI patients was significantly higher than that of healthy controls. In SCI patients, the relative abundance of Lachnospiraceae (related to lymphocyte subset regulation), Ruminococcaceae (closely related to central nervous system diseases), and Escherichia-Shigella (closely related to intestinal infections) increased significantly, while the butyrate producing bacteria (Fusobacterium) that were beneficial to the gut were dramatically decreased. Correlation analysis showed that the five bacterial genera of SCI patients, including Lachnospiraceae UCG-008, Lachnoclostridium 12, Tyzzerella 3, Eubacterium eligens group, and Rumencocciucg-002, were correlated with T lymphocyte subsets and NK cells. In the SCI group, the flora Prevotella 9, Lachnospiraceae NC2004 group, Veillonella, and Sutterella were positively correlated with B cells. However, Fusobacterium and Akkermansia were negatively correlated with B cells. Moreover, Roseburia and Ruminococcaceae UCG-003 were positively correlated with CIK cells. Our results suggest that the gut microbiota of patients with SCI is associated with lymphocyte subsets. Therefore, it is possible to improve immune dysregulation in SCI patients by modulating gut microbiota, which may serve as a new therapeutic method for SCI.