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Purpose <p>Knee osteoarthritis (OA) is a complex disease characterized by inflammation, cartilage degeneration, and tissue remodeling. Although many biomarkers have been investigated, the combined biological significance of systemic and local biomarkers is not fully understood. This study aimed to comprehensively address serum and synovial biomarkers associated with inflammation, cartilage turnover, and metabolic regulation in patients with knee OA.</p> Methods <p>Seventy-five participants (40 knee OA patients and 35 surgical controls) were prospectively included in the study. Serum and synovial fluid levels of IL-6, TNF-α, COMP, PIINP, Spexin, Asprosin, and Klotho were measured using ELISA. Intergroup comparisons were performed using the Mann-Whitney U test, correlations were evaluated using Spearman correlation analysis, and diagnostic performance was assessed using receiver operating characteristic (ROC) analysis.</p> Results <p>Serum IL-6 demonstrated excellent diagnostic performance, showing the highest discriminative ability (AUC = 0.962). Serum Spexin (AUC = 0.782) and COMP (AUC = 0.661) showed moderate discriminative performance. In synovial fluid, COMP (AUC = 0.733), IL-6 (AUC = 0.701), and PIINP (AUC = 0.687) showed significant but moderate diagnostic accuracy. Correlation analyses indicated that there may be significant associations between adipokines and inflammatory and cartilage-related biomarkers.</p> Conclusion <p>Integrated evaluation of serum and synovial biomarkers supports the concept that osteoarthritis involves inflammatory and tissue remodeling pathways. IL-6 showed the strongest discriminative performance overall, while COMP and PIINP reflected the stage-dependent cartilage remodeling process. These findings improve the biological characterization of advanced knee osteoarthritis and may contribute to future biomarker-based patient classification methods.</p>

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Integrated serum and synovial biomarker profiling reveals inflammatory and cartilage remodeling signatures in age-restricted knee osteoarthritis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月1日

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  • Sefa Key,
  • Erhan Yılmaz,
  • Huseyin Fatih Gul,
  • Anıl Agar,
  • Omer Esmez,
  • Osman Sedat Tanyeri,
  • Mehmet Onur Kaya,
  • Nurgul Cetgen Key

摘要

Purpose

Knee osteoarthritis (OA) is a complex disease characterized by inflammation, cartilage degeneration, and tissue remodeling. Although many biomarkers have been investigated, the combined biological significance of systemic and local biomarkers is not fully understood. This study aimed to comprehensively address serum and synovial biomarkers associated with inflammation, cartilage turnover, and metabolic regulation in patients with knee OA.

Methods

Seventy-five participants (40 knee OA patients and 35 surgical controls) were prospectively included in the study. Serum and synovial fluid levels of IL-6, TNF-α, COMP, PIINP, Spexin, Asprosin, and Klotho were measured using ELISA. Intergroup comparisons were performed using the Mann-Whitney U test, correlations were evaluated using Spearman correlation analysis, and diagnostic performance was assessed using receiver operating characteristic (ROC) analysis.

Results

Serum IL-6 demonstrated excellent diagnostic performance, showing the highest discriminative ability (AUC = 0.962). Serum Spexin (AUC = 0.782) and COMP (AUC = 0.661) showed moderate discriminative performance. In synovial fluid, COMP (AUC = 0.733), IL-6 (AUC = 0.701), and PIINP (AUC = 0.687) showed significant but moderate diagnostic accuracy. Correlation analyses indicated that there may be significant associations between adipokines and inflammatory and cartilage-related biomarkers.

Conclusion

Integrated evaluation of serum and synovial biomarkers supports the concept that osteoarthritis involves inflammatory and tissue remodeling pathways. IL-6 showed the strongest discriminative performance overall, while COMP and PIINP reflected the stage-dependent cartilage remodeling process. These findings improve the biological characterization of advanced knee osteoarthritis and may contribute to future biomarker-based patient classification methods.