Diagnostic Features of Discogenic Low Back Pain: A Narrative Review and Suggested Areas for Future Research and Clinical Practice.

Abstract

Chronic low back pain is a leading cause of global disability, with discogenic low back pain (DLBP), mechanically stimulated pain arising from the intervertebral disc, representing an important but often under-recognized subtype. DLBP arises from chronic structural and metabolic changes within the intervertebral disc, leading to inflammation, nerve ingrowth, and nociceptive sensitization. Diagnosis remains difficult due to overlapping symptoms, non-specific imaging, and the lack of a universally accepted reference standard. Accurate identification is critical for guiding effective management. This review aims to summarize and evaluate current and proposed diagnostic features of DLBP. A narrative literature review was conducted using PubMed, Scopus, and Web of Science, with supplementary hand searching and citation tracking. Studies evaluating clinical or investigative diagnostic features of DLBP were included, without restriction on study design. Studies involving neural compression, radiculopathy, or non-DLBP were excluded. Evidence was thematically synthesized, with preference given to higher-quality or larger studies where findings overlapped. No clinical features have been consistently validated as predictive of DLBP. Flexion-based postures, movements, and prolonged sitting demonstrate modest diagnostic value, while traditional features such as axial loading or Valsalva maneuvers lack supporting evidence. Imaging findings, including Modic changes, high-intensity zones, and Pfirrmann grading, show limited specificity. Provocative discography is constrained by invasiveness, complications, and questionable reliability. Emerging metabolic imaging techniques demonstrate early potential as future reference standards. Evidence of clinical prediction models for DLBP remains limited. DLBP diagnosis remains constrained by non-specific features and imperfect reference standards. Emerging metabolic imaging techniques warrant further validation, alongside development of robust diagnostic models to improve clinical decision making.