High serum creatinine is associated with reduction of psychiatric symptoms in female patients with anti-NMDAR encephalitis

Development of a prediction model integrating PD-1 and ICOS for early differential diagnosis between autoimmune and viral encephalitis

Background

Early diagnosis and treatment for encephalitis are crucial for improving patient outcomes and reducing the economic burden, especially given the overlapping symptoms and low specificity of auxiliary diagnostic tests between viral encephalitis (VE) and autoimmune encephalitis (AE). Since these two conditions require different treatment approaches, an early differential diagnosis between AE and VE is a critical challenge.

Methods

This study enrolled a cohort of 75 patients (38 with VE and 37 with AE) between September 2022 and July 2024. The demographic data, clinical characteristics, and laboratory test results were collected. The expression levels of co-stimulatory molecules were detected by flow cytometry and enzyme-linked immunosorbent assay within 7 days for viral encephalitis and 90 days for autoimmune encephalitis in the early phase of the disease. Differential analysis, logistic regression analysis, and least absolute shrinkage and selection operator regression were employed for model construction. Finally, a nomogram and a receiver operating characteristic (ROC) curve were developed to visualize the model and evaluate its predictive accuracy.

Results

Upon analyzing the collected data, a model for the early differential diagnosis between AE and VE was eventually established. This comprehensive model incorporated 10 variables: serum creatinine and chloride levels, the percentage of peripheral blood CD4+ICOS+ and CD19+PD-L1+, plasma soluble inducible costimulatory ligand (sICOSL), cerebrospinal fluid (CSF) glucose content, and the presence of fever, nausea, vomiting, headaches, and cognitive impairment. Patients with creatinine <60.75 (μmol/L), chloride <106.25 (mmol/L), CD4+ICOS+ ≥11.2%, CD19+PD-L1+ ≥12.35%, plasma sICOSL≥286.37 ng/mL, CSF sugar content ≥3.775 (mmol/L), and those with cognitive impairment are more likely to be diagnosed with AE. The area under the curve (AUC)-ROC of our model was 0.942 [95% confidence interval (CI): 0.887-0.997], with a sensitivity of 0.844 and a specificity of 0.971, indicating strong diagnostic performance.

Conclusion

This diagnostic model offers a convenient tool for distinguishing AE from VE in the early phase, facilitating early diagnosis and treatment, improving patient prognosis, and reducing financial burdens.

Mendelian randomization study of the relationship between blood and urine biomarkers and schizophrenia in the UK Biobank cohort

BACKGROUND

The identification of suitable biomarkers is of crucial clinical importance for the early diagnosis of treatment-resistant schizophrenia (TRS). This study aims to comprehensively analyze the association between TRS and blood and urine biomarkers.

METHODS

Candidate TRS-related single nucleotide polymorphisms (SNPs) were obtained from a recent genome-wide association study. The UK Biobank cohort, comprising 376,807 subjects with blood and urine biomarker testing data, was used to calculate the polygenic risk score (PRS) for TRS. Pearson correlation analyses were performed to evaluate the correlation between TRS PRS and each of the biomarkers, using calculated TRS PRS as the instrumental variables. Bidirectional two-sample Mendelian randomization (MR) was used to assess potential causal associations between candidate biomarkers with TRS.

RESULTS

Here we identify a significant association between TRS PRS and phosphate (r = 0.007, P = 1.96 × 10-4). Sex subgroup analyses identify seven and three candidate biomarkers associated with TRS PRS in male and female participants, respectively. For example, total protein and phosphate for males, creatinine and phosphate for females. Bidirectional two-sample MR analyses indicate that TRS is negatively associated with cholesterol (estimate = -0.363, P  = 0.008). Conversely, TRS is positively associated with total protein (estimate = 0.137, P  = 0.027), mean corpuscular volume (estimate = 0.032, P  = 2.25 × 10-5), and mean corpuscular hemoglobin (estimate = 0.018, P  = 0.007).

CONCLUSIONS

Our findings provide insights into the roles of blood and urine biomarkers in the early detection and treatment of TRS.