Frontiers in immunology · 2025
Precision prediction of intervertebral disc degeneration in ankylosing spondylitis using a nomogram model reveals the pivotal role of Th2-type immune dysregulation.
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Wang Xiao-Nan, Ma Xiao-Tian, Li Jie, Zhou Run-Tian, Jin Yuan-Zhang, Zhao Xiao-Feng, Jing Dou-Dou, Zhao Bin
Аннотация
BACKGROUND: Ankylosing spondylitis (AS) is an immune-mediated chronic inflammatory disease. When AS is complicated by intervertebral disc degeneration (IVDD), disease complexity increases substantially, often resulting in poor long-term outcomes. Although previous studies have explored the mechanisms linking AS and IVDD, reliable tools for precise risk prediction and early intervention remain scarce.
METHODS: In this retrospective study, we enrolled 144 patients with AS (60 with and 84 without IVDD). Their clinical features, immune status, and inflammatory cytokine levels were analyzed. A nomogram prediction model was constructed using multivariable logistic regression. Model performance was evaluated via receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
RESULTS: Multivariable analysis identified body mass index (BMI), peripheral blood Th2 cell percentage (Th2%), and serum IL-4 levels as independent risk factors for IVDD in patients with AS. All associations remained statistically significant after Benjamini-Hochberg correction (BMI: BH-adjusted P = 0.001; Th2%: BH-adjusted P = 0.019; IL-4: BH-adjusted P = 0.019). Incorporation of these factors into a nomogram yielded excellent discriminative performance (area under the curve, AUC = 0.83) and calibration, outperforming a simplified model based solely on BMI (AUC = 0.74). This improvement in predictive accuracy was statistically significant, as determined by DeLong's test (P = 0.018). DCA revealed that at a threshold probability of 60.8%, the nomogram effectively distinguished high-risk patients, underscoring its strong clinical applicability.
CONCLUSIONS: This study is the first to highlight the critical roles of Th2 cells and IL-4 in AS complicated by IVDD, and establishes a nomogram that accurately predicts the risk of IVDD in AS. Beyond offering a tool for early detection and personalized management, these findings open avenues for investigating overlapping pathogenic mechanisms and potential immunotherapeutic targets in AS-IVDD.
METHODS: In this retrospective study, we enrolled 144 patients with AS (60 with and 84 without IVDD). Their clinical features, immune status, and inflammatory cytokine levels were analyzed. A nomogram prediction model was constructed using multivariable logistic regression. Model performance was evaluated via receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).
RESULTS: Multivariable analysis identified body mass index (BMI), peripheral blood Th2 cell percentage (Th2%), and serum IL-4 levels as independent risk factors for IVDD in patients with AS. All associations remained statistically significant after Benjamini-Hochberg correction (BMI: BH-adjusted P = 0.001; Th2%: BH-adjusted P = 0.019; IL-4: BH-adjusted P = 0.019). Incorporation of these factors into a nomogram yielded excellent discriminative performance (area under the curve, AUC = 0.83) and calibration, outperforming a simplified model based solely on BMI (AUC = 0.74). This improvement in predictive accuracy was statistically significant, as determined by DeLong's test (P = 0.018). DCA revealed that at a threshold probability of 60.8%, the nomogram effectively distinguished high-risk patients, underscoring its strong clinical applicability.
CONCLUSIONS: This study is the first to highlight the critical roles of Th2 cells and IL-4 in AS complicated by IVDD, and establishes a nomogram that accurately predicts the risk of IVDD in AS. Beyond offering a tool for early detection and personalized management, these findings open avenues for investigating overlapping pathogenic mechanisms and potential immunotherapeutic targets in AS-IVDD.