Introduction: Diseases related to chronic persisting inflammation are amongst the largest sources of morbidity and health costs, yet biomarkers for early diagnosis, prognosis, and treatment response are not sufficiently effective.
Areas covered: This narrative review discusses how inflammation concepts have evolved from ancient times to the present, and places in perspective the use of blood-based biomarkers to assess chronic inflammatory diseases. From reviews of biomarkers in specific diseases, emerging biomarker classifiers and their clinical utility is discussed. Biomarkers representative of systemic inflammatory response such as C Reactive Protein are distinguished from local tissue inflammation markers such as cell membrane components and molecules involved in matrix degradation. The application of newer methodologies such as gene signatures, non-coding RNA, and artificial intelligence/machine-learning techniques is highlighted.
Expert opinion: The dearth of novel biomarkers for chronic inflammatory diseases can be ascribed in part to the lack of basic understanding about non-resolving inflammation, and in part by fragmentation of effort whereby individual diseases are studied but their pathophysiologic commonalities and differences are neglected. Finding better blood biomarkers for chronic inflammatory diseases may be best addressed by studying cell and tissue products of local inflammation, augmenting data interpretation by artificial intelligence techniques.
Keywords: Artificial intelligence; axial spondyloarthritis; biomarkers; chronic inflammation; inflammation pattern; inflammatory bowel disease; laboratory diagnostics; non-resolving inflammation.