Advances to Establish Biomarkers Predictive of Opioids use Disorder in Patients with Chronic Pain

Chronic pain has become an increasingly prevalent condition in today's world, and the use of opioids remains one of the main strategies for managing this type of pain. In this context, the search for predictive biomarkers of opioid dependence in patients with chronic pain represents an urgent clinical need, given the growing use of these drugs and the risks associated with long-term treatment. Although several advances have been made in the field of pain neurobiology, the literature remains scarce and heterogeneous, requiring a multidisciplinary and systematic approach to consolidate current evidence and outline new investigative pathways. This is a literature review that searched the PubMed, SciELO, and Cochrane databases using the descriptors “chronic pain,” “biomarkers,”, “neuronal alterations” and “opioids.” Article selection was performed first by title and then by full-text screening. Two authors independently evaluated the articles, followed by full-text selection by all authors. The results highlight different types of biomarkers with predictive potential for opioid use disorder. Alterations in the availability of µ-opioid receptors (MOR) in the central nervous system—particularly in regions such as the amygdala and nucleus accumbens—were identified and associated with a higher risk of misuse. Specific microRNAs, such as let-7, miR-103/107, and miR-146a, were also evidenced, being involved in the negative regulation of MOR and in the modulation of inflammatory and neuroplastic processes. Additionally, genetic variants associated with a predisposition to problematic opioid use were observed, as well as peripheral immunological biomarkers such as IL-6 and TNF-α, and metabolites like quinolinate. The integration of these findings suggests that multiple systems—genetic, immunological, and neurofunctional—are involved in vulnerability to dependence. The discussion of the findings emphasizes the clinical relevance of integrating molecular, genetic, epigenetic, and neuroimaging data in the development of biomarker panels applicable to the monitoring of patients with chronic pain. Tools such as RT-qPCR, flow cytometry, and quantitative sensory testing, combined with machine learning algorithms, emerge as promising strategies to enable a personalized, safe, and effective approach to pain management and dependence prevention.

Conclusion: The incorporation of predictive biomarkers of opioid dependence into the clinical evaluation of patients with chronic pain is a promising path for monitoring opioid use and promoting personalized medicine. The path to their implementation must involve extensive study of these biomarkers. Further research, including studies involving chronic pain populations, is needed to consolidate the clinical applicability of these biomarkers.

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The authors declare no conflict of interest.

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The datasets used in this study are openly available at [repository link] and the source code is available on GitHub at [GitHub link].

This work did not receive any external funding.