# Elevated Serum Levels of YKL-40, YKL-39, and SI-CLP in Patients with Treatment Failure to DMARDs in Patients with Rheumatoid Arthritis

## TL;DR

This study found that higher levels of certain proteins in the blood may predict poor response to arthritis treatments in rheumatoid arthritis patients.

## Contribution

The study identifies chitinase-like proteins (YKL-40, YKL-39, SI-CLP) as potential biomarkers for treatment failure in rheumatoid arthritis patients.

## Key findings

- Non-responders to DMARDs had significantly higher serum levels of YKL-40, YKL-39, and SI-CLP.
- Adding CLPs to regression models improved diagnostic accuracy for treatment failure.
- 35.86% of non-responders had elevated levels of all three CLPs.

## Abstract

Around 30–60% of patients with rheumatoid arthritis (RA) present treatment failure to conventional synthetic disease-modifying antirheumatic drugs (csDMARDs). Chitinase-like proteins (CLPs) (YKL-40, YKL-39, SI-CLP) might play a role, as they are associated with the inflammatory process. This study aimed to evaluate CLP utility as a biomarker in the treatment failure of csDMARDs. A case–control study included 175 RA patients classified into two groups based on therapeutic response according to DAS28-ESR: responders (DAS28 < 3.2); non-responders (DAS28 ≥ 3.2). CLP serum levels were determined by ELISA. Multivariable logistic regression and receiver operating characteristic (ROC) curves were used to evaluate CLPs’ utility as biomarkers of treatment failure. Non-responders presented higher levels of YKL-40, YKL-39, and SI-CLP compared with responders (all: p < 0.001). YKL-40 correlated positively with YKL-39 (rho = 0.39, p < 0.001) and SI-CLP (rho = 0.23, p = 0.011) and YKL-39 with SI-CLP (rho = 0.34, p < 0.001). The addition of CLPs to the regression models improves diagnostic accuracy (AUC 0.918) compared to models including only clinical classical variables (AUC 0.806) p < 0.001. Non-responders were positive for all CLPs in 35.86%. Conclusions: CLPs could be considered as a useful biomarker to assess treatment failure, due to their association with clinical variables and improvement to the performance of regression models.