# The diagnostic and prognostic value of CXCL13, CXCL10, and CXCL8 in patients with neurosyphilis

## TL;DR

This study investigates the usefulness of three biomarkers in diagnosing and predicting outcomes for neurosyphilis patients.

## Contribution

The study introduces a new diagnostic model based on CSF-CXCL13 for improved neurosyphilis detection.

## Key findings

- CSF-CXCL13 showed high accuracy in distinguishing neurosyphilis from non-neurosyphilis and CNS infections.
- A predictive model (MODEL1) based on CSF-CXCL13 demonstrated strong diagnostic performance and clinical benefits.
- CSF-CXCL10 and CSF-CXCL8 had lower diagnostic accuracy compared to CSF-CXCL13.

## Abstract

The purpose of this study is to examine the diagnostic and therapeutic value of CXCL13 (CSF-CXCL13), CXCL10 (CSF-CXCL10), and CXCL8 (CSF-CXCXCL8) in NS patients in a systematic manner.

The study will include individuals who are the first to undergo neurosyphilis (NS) screening from August 2023 to October 2024, and will gather demographic, clinical, and laboratory data, as well as cerebrospinal fluid (CSF) and blood samples. Enzyme-linked immunosorbent assay (ELISA) was used to quantitatively detect the concentrations of CXCL13, CXCL10, and CXCL8 in CSF and blood samples. Use receiver operating characteristic (ROC) curves to evaluate the ability of cytokines to distinguish between NS and non-NS individuals, and further evaluate in different populations, including the total population, People Living with HIV(PLWH), Non-People Living with HIV(Non-PLWH) population. Develop an NS diagnostic model using logistic regression analysis results, and ensure the model is valid by conducting 5-fold cross-validation, calibration curve, and clinical decision curve (DCA). Use a Nomogram to visualize the model.

A total of 233 participants were included in the study. ROC shows that the area under the curve (AUC) of CSF-CXCL13 in distinguishing NS from Non-NS,#x3001; NS from CNS infections is 0.812 and 0.839, respectively. In contrast, the AUC of CSF-CXCL10 and CSF-CXCL8 in distinguishing NS from Non-NS were 0.568 and 0.638, respectively. The AUC in distinguishing NS from other CNS infections were 0.604 and 0.556, respectively. To enhance the effectiveness of differential diagnosis, we employed logistic regression analysis to screen variables and developed a predictive model MODEL1. The results showed that the AUC value of MODEL1 was 0.888, and the calibration curve and DCA curve demonstrated good accuracy and clinical benefits of the model, demonstrating good predictive performance. After NS treatment, the levels of CSF-CXCL13, CSF-CXCL10, and CSF-CXCL8 slightly decreased.

CSF-CXCL13 has good differential value in distinguishing NS from Non-NS, NS from CNS infections, while CSF-CXCL10 and CSF-CXCL8 have lower differential sensitivity. The diagnostic performance of the NS diagnostic model (Model 1) based on CSF-CXCL13 has been improved.