Inflammatory and Nutritional Markers as Indicators for Diagnosing and Assessing Disease Activity in MS and NMOSD.

Background: Inflammation and nutritional markers have recently gained recognition for their roles in the fabrication of cognitive control centers demyelinating illnesses. Inflammatory indices such as the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammatory index (SII), and systemic inflammatory response index (SIRI), along with nutritional markers like albumin (ALB), hemoglobin (HB), and body mass index (BMI), may predict disease occurrence. However, their potential in evaluating diseases such as multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) remains unexplored. Methods: We retrospectively evaluated 249 NMOSD patients, 244 MS patients, and 249 healthy controls (HC), calculating MLR, NLR, PLR, SII, and SIRI, and measuring ALB, HB, and BMI levels. Logistic regression and ROC curves were used to develop and validate models for diagnosing and differentiating MS and NMOSD. Further, 35 MS patients, 38 NMOSD patients, and 85 matched HC were recruited for validation, and marker changes were monitored over six months. Results: Comparing MS and NMOSD groups with HC, MLR, NLR, SII, and SIRI were significantly greater, while ALB levels were lower (P<0.05). NMOSD patients exhibited higher MLR, NLR, SII, and SIRI, and lower HB and ALB levels contrasted with MS patients (P<0.05). These markers correlated negatively with total T lymphocytes and positively with C-reactive protein, the Expanded Disability Status Scale (EDSS), and MRI T2 lesion count. Following remission, NLR, SII, and SIRI decreased, while ALB increased over six months (P<0.05). Diagnostic models based on these markers showed AUCs of 0.840 (95% CI:0.806-0.875) for MS and 0.905 (95% CI:0.877-0.933) for NMOSD. Differential diagnosis between MS and NMOSD showed an AUC of 0.806 (95% CI: 0.750-0.863). Conclusion: Inflammatory and nutritional markers are promising for assessing disease activity in MS and NMOSD. Diagnostic models based on these markers enhance the accuracy and clinical value of differentiating between the two conditions.