From Association to Prediction: Translational Barriers in Pain Biomarker Research

Pain biomarkers have been proposed as potential tools to improve patient stratification, treatment selection, and individualized therapeutic strategies in chronic pain. However, despite an increasing volume of research across neuroimaging, electrophysiological, and molecular domains, their translation into clinical practice remains limited. A central challenge lies in the conceptual and methodological misalignment between biomarker discovery and clinical applicability. Many studies labelled as "predictive" rely on measurements obtained during or after intervention, small sample sizes, or lack of external validation, limiting their ability to inform real-world decision-making. In addition, the distinction between predictive, monitoring, and mechanistic biomarkers is often blurred, further complicating interpretation and implementation. This Perspective examines why many candidate pain biomarkers, although biologically informative, have not yet become clinically actionable tools for treatment selection. We distinguish between associative, mechanistic, monitoring, preventive, and truly predictive biomarkers using clinically relevant examples from pain research, and we outline the methodological requirements needed to translate biomarker discovery into precision pain medicine. We argue that the field requires a more rigorous framework for defining and validating predictive biomarkers, encompassing appropriate timing of measurement, robust study design, external validation, and patient-relevant endpoints. Without this framework, pain biomarker research risks continuing to generate biologically informative but clinically non-actionable findings that do not advance individualized therapeutic decision-making.