Intervertebral disc degeneration and how it leads to low back pain

• Published in: JOR-spine Vol. 6; no. 1; pp. e1231 - n/a
• Main Authors: Diwan, Ashish D., Melrose, James
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.03.2023; Wiley
• Subjects: artificial intelligence; Back pain; Cartilage; Chronic pain; Cytokines; Degenerative disc disease; discogenic pain; facial recognition; Flexibility; GDF6; intervertebral disc degeneration; Ligaments; low back pain; Muscle pain; neuropathic pain; nociceptive pain; Population; Review; Reviews; Spinal cord; intervertebral disc degeneration; neuropathic pain; nociceptive pain; low back pain; facial recognition; GDF6; artificial intelligence; discogenic pain
• ISSN: 2572-1143; 2572-1143
• DOI: 10.1002/jsp2.1231

The purpose of this review was to evaluate data generated by animal models of intervertebral disc (IVD) degeneration published in the last decade and show how this has made invaluable contributions to the identification of molecular events occurring in and contributing to pain generation. IVD degeneration and associated spinal pain is a complex multifactorial process, its complexity poses difficulties in the selection of the most appropriate therapeutic target to focus on of many potential candidates in the formulation of strategies to alleviate pain perception and to effect disc repair and regeneration and the prevention of associated neuropathic and nociceptive pain. Nerve ingrowth and increased numbers of nociceptors and mechanoreceptors in the degenerate IVD are mechanically stimulated in the biomechanically incompetent abnormally loaded degenerate IVD leading to increased generation of low back pain. Maintenance of a healthy IVD is, thus, an important preventative measure that warrants further investigation to preclude the generation of low back pain. Recent studies with growth and differentiation factor 6 in IVD puncture and multi‐level IVD degeneration models and a rat xenograft radiculopathy pain model have shown it has considerable potential in the prevention of further deterioration in degenerate IVDs, has regenerative properties that promote recovery of normal IVD architectural functional organization and inhibits the generation of inflammatory mediators that lead to disc degeneration and the generation of low back pain. Human clinical trials are warranted and eagerly anticipated with this compound to assess its efficacy in the treatment of IVD degeneration and the prevention of the generation of low back pain. Data generated by animal models over the last decade has made invaluable contributions to the identification of molecular events occurring in and contributing to intervertebral disc (IVD) degeneration and pain generation and has identified several potential therapeutic targets. IVD degeneration and associated spinal pain is a complex multifactorial event, its complexity poses difficulties in the selection of the most appropriate therapeutic target of many potential candidates to focus on in the formulation of strategies to effect disc repair and regeneration and the alleviation of associated neuropathic and nociceptive pain. Application of artificial intelligence in facial recognition has improved the assessment of pain in animal models. Deep machine learning and neural networking have also been applied in the analysis of data generated from molecular signaling pathways involved in disc degeneration and pain generation. This methodology has predictive capability, can be automated and offers a superior unbiased assessment of data compared to observer‐based classifications and clinical prognosis data.