Why and How Should We Integrate Biomarkers into Complex Trials? A Discussion on Paradigms and Clinical Research Strategies

Abstract

Background: Research in complementary and alternative medicine (CAM) encounters a variety of challenges, such as potentially synergistic, multimodal, and complex interventions which are often dependent on the relationship between practitioner and patient, on specific settings, and on patients’ individual preferences, expectations, beliefs, and motivations. Moreover, patients seeking CAM care often suffer from chronic disease conditions, and multiple symptoms and/or pathologies. On the other hand, CAM interventions are often challenged as being solely dependent on subjective and nonspecific factors without biologically based mechanisms of action. If we agree that biomarkers as outcomes are important for the understanding of CAM interventions, a hypothesis- and strategy-driven process for the selection of the most appropriate biomarkers is needed. Methods: This paper presents the results of an expert panel on how to integrate biomarkers in whole system research of an interdisciplinary workshop on research methodology in CAM held in November 2012. Results: The following main CAM research challenges were identified: (a) finding appropriate biomarkers, which are able to picture the complex pathophysiological pathways and likewise complex interventions under study; (b) integrating these biomarkers into clinical trials in CAM; and (c) identifying the biomarkers specific to the particular CAM intervention being applied. Conclusion: The paper provides a disease/condition/symptom- and intervention-driven strategy regarding how to identify the outcomes of interest and possible related biomarkers. The research approach presented here allows the selected biomarkers to be grounded in conventional physiology/pathophysiology as well as complementary and alternative concepts, including traditional systems of medicine. The goal is to provide researchers in the field with a framework on how to integrate biomarkers into complex trials.