Purpose: Inflammation is integral to the pathogenesis of intervertebral disc degeneration, yet the role of systemic inflammatory markers in this process remains underexplored. This study aims to explore the association between the Neutrophil-to-Lymphocyte Ratio (NLR) and the severity of disc degeneration.
Patients and methods: A retrospective analysis was conducted on 375 patients diagnosed with lumbar disc degeneration between April 2018 and May 2021. All patients underwent a complete blood cell count examination. We applied the Pfirrmann grading system for cumulative disc grading, stratifying patients into two groups: a high-score group (cumulative grade > 17) and a low-score group (cumulative grade ≤ 17), based on the median cumulative grade. The association between the NLR and and the severity of disc degeneration was further analyzed using correlation analysis and logistic regression models. Furthermore, the predictive capacity of the NLR for lumbar disc degeneration was assessed using the Receiver Operating Characteristic (ROC) curve.
Results: We found a significant positive correlation between high NLR levels and severe disc degeneration. The high-score group exhibited a significantly higher NLR compared to the low-score group [2.63 (1.91-4.18) vs. 2.04 (1.38-2.74), respectively, p < 0.001]. Significant correlations were found between NLR and patient characteristics (including age, BMI, VAS, NSAIDs usage, hemoglobin) and the cumulative grading. Logistic regression analysis identified age and NLR as independent predictors of the severity of disc degeneration. The ROC curve analysis demonstrated a good predictive capability of NLR for lumbar disc degeneration.
Conclusion: NLR could serve as a promising biomarker for assessing the severity of lumbar disc degeneration and offer potential benefits in both early diagnosis and treatment strategies.
Keywords: biomarker; intervertebral disc degeneration; neutrophil-to-lymphocyte ratio; retrospective cohort study; systemic inflammation.
Copyright © 2024 Guo, Zeng, Lu, Guo, Shan, Huang and Wu.