Toward Precision Acupuncture for Pain: Host Genetic Variability, Omics Biomarkers, and Treatment-Response...

Pain is a heterogeneous clinical condition characterized by substantial interindividual variability in symptom severity and treatment response. Acupuncture has been widely used for the management of various pain disorders, including chronic musculoskeletal pain, migraine, and cancer-related pain. However, clinical outcomes remain highly variable across patients, suggesting that average treatment effects may not fully capture biologically and clinically meaningful response heterogeneity. Recent advances in human genetics and multiomics technologies have provided new opportunities to investigate the biological factors that may contribute to this variability. Current genetic evidence, derived mainly from candidate-gene studies, suggests that polymorphisms involved in pain perception and neuromodulatory pathways, including COMT and OPRM1, may influence individual sensitivity to acupuncture analgesia; however, these findings remain exploratory and require validation in larger and more diverse cohorts. In parallel, transcriptomic, epigenetic, proteomic, metabolomic, and inflammatory profiling studies have identified molecular changes associated with acupuncture treatment. These treatment-associated signals should be distinguished from predictive biomarkers: Baseline genetic or molecular features may help estimate the likelihood of response, whereas posttreatment molecular alterations more often reflect treatment engagement, biological adaptation, or downstream mechanistic effects. Although the available evidence remains fragmented and is often limited by small sample sizes, heterogeneous acupuncture protocols, variable analytical pipelines, and insufficient external validation, it provides a useful foundation for developing biomarker-informed approaches to acupuncture research. In this review, we summarize current evidence linking host genetic variability and omics-derived molecular signatures to acupuncture analgesia, clarify the conceptual distinction between predictive and treatment-associated biomarkers, and discuss the potential and limitations of response-stratified acupuncture. We further highlight key priorities for the field, including standardized treatment protocols, multicenter cohorts, prospective biospecimen collection, reproducible omics workflows, and external validation of prediction models. Together, these considerations support precision acupuncture as an emerging research framework for understanding and eventually improving individualized pain management, rather than as a currently established clinical strategy.