Serum IL-19 and Biglycan as Candidate Biomarkers by Proteomic Approach in Patients with Severe Low Back Pain-a Pilot Study

OBJECTIVE: Low back pain (LBP) is common. Although imaging studies are widely used to evaluate back pain, the link between pain and intervertebral disc degeneration visualized on images is unclear. This study aims to discover novel biomarkers in patients with severe back pain. DESIGN: Levels of 1512 serum proteins from 29 patients with LBP and 11 healthy controls were compared. Machine learning analyses were employed to identify proteins diagnostic of LBP, based on cumulative feature importance across five machine learning models with the highest performance. Proteins with highest feature importance and area under the receiver-operating characteristic curves (AUC) underwent pathway and network analyses. RESULTS: Distinct serum biomarkers differentiating LBP patients from healthy controls were identified. Using Somascan proteomic analysis and machine learning methods, 37 significant biomarkers were highlighted. Notably, IL-19 and biglycan showed the strongest diagnostic potential (AUC 0.91 and 0.89, respectively). Pathway analyses implicate systemic processes in LBP pathology. CONCLUSIONS: IL-19 and biglycan showed promise as serum biomarkers in patients with severe LBP. Given the small sample size of this study, these findings are preliminary and require validation in larger, age- and sex-matched cohorts, with a confirmative, quantitative assay for key markers.