Albumin and Gamma-Glutamyl Transferase as Biomarkers for Differentiating Systemic Juvenile Idiopathic Arthritis from Reactive Arthritis

Abstract

Objective: Systemic juvenile idiopathic arthritis (sJIA) and reactive arthritis (ReA) share overlapping clinical features, posing diagnostic challenges. Early differentiation is critical for treatment decisions but lacks reliable biomarkers. This study aims to identify simple clinical indicators and develop a clinical prediction model to distinguish sJIA from ReA.

Methods: This study retrospectively included clinical data of 397 sJIA patients and 290 ReA patients who attended the Children's Hospital of Chongqing Medical University from 2016-2024. Key predictors were identified by ANOVA, chi-square tests, univariate logistic regression, multivariate logistic regression, and stepwise analysis. The diagnostic model was established and validated by performing ROC analysis. Furthermore, we additionally included data from 20 sJIA and 20 ReA patients from two other centers to validate the above results.

Results: A total of 19 statistically different clinical indicators were identified by ANOVA and chi-square tests. These indicators were included in univariate and multivariate logistic regression analyses, lower albumin levels and significantly higher levels of gamma-glutamyl transferase (GGT) were found in sJIA patients compared to ReA in both the training and validation sets (p values were all < 0.05). In a stepwise analysis of age, gender, inflammatory cells (lymphocytes, monocytes) and inflammatory markers, it was found that albumin and GGT were still effective in differentiating between the two diseases. Clinical prediction models were developed using albumin and GGT, with AUCs of 0.842 (training) and 0.849 (validation), showing excellent diagnostic effect. These indicators also demonstrated good diagnostic efficacy in cohorts from two other centers.

Conclusion: Albumin and GGT are important clinical indicators for differentiating sJIA from ReA. The albumin-GGT prediction model provides a simple, clinically feasible tool to reduce diagnostic uncertainty.

Keywords: albumin; clinical prediction model; gamma-glutamyl transferase; reactive arthritis; systemic juvenile idiopathic arthritis.

Figure 1

Flow chart of the research process.

Figure 2

Intersection of clinical indicators of difference between training set, validation set, and overall dataset.

Figure 3

Stepwise analysis of the relationship between clinical indicators and inflammatory indicators in training set. (A) Stepwise analysis of the relationship between GGT and inflammatory indicators in training set; (B) Stepwise analysis of the relationship between Albumin and inflammatory indicators in training set. Model 1: clinical indicators plus adjusted for the age and sex; Model 2: Model 1 plus adjusted for the immune cell count (white blood cell, monocyte, lymphocyte, neutrophil); Model 3: Model 2 plus adjusted for the classical inflammatory indicators (CRP, ESR).

Figure 4

Predictive modeling of GGT in combination with albumin for the prediction of sJIA. (A) ROC curves in the training set; (B) ROC curves in the validation set; (C) ROC curves in the external validation set.