Biomarkers to predict, prevent, and treat persistent pain: omics
Mark R. Hutchinson, Daniel T. Barratt, Charlotte H. Johnston, Melissa Humphries, Carolyn Semmler, G. C. Tettamanzi, Andrew D. Greentree, Paul Rolan
Abstract
ABSTRACT: Persistent pain represents a significant global health challenge, necessitating innovative biomarker technologies that facilitate personalised prediction, prevention, and treatment. Recent advances in omics, encompassing genomics, proteomics, transcriptomics, lipidomics, epigenomics, and metabolomics, now permit high-resolution mapping of neuroimmune pathways implicated in pain chronification. Yet, biomarkers must transcend isolated molecular or sensory indicators, integrating emotional, cognitive, functional, and social dimensions of pain. Emerging quantum sensing technologies, such as diamond nitrogen-vacancy sensors and portable magnetoencephalography systems, promise precise and wearable tools capable of real-time, multimodal assessment of pain. Concurrently, transparent machine learning methods combining explainable artificial intelligence with physiologically informed modelling are crucial for managing the vast data complexity inherent to these multidimensional omics approaches. Ultimately, achieving economically viable, environmentally sustainable, and universally accessible pain management solutions requires strategically streamlined methods. Here, we outline a visionary framework of measurement-enabled understanding that enables precision pain medicine with rapid feedback that points toward actionable clinical outcomes, harnessing interdisciplinary innovation to address persistent pain comprehensively, just as genomics and immunotherapy have transformed cancer care.