Possible Gastroenterological Causes of FUO (Fever of Unknown Origin)

Fever of unknown origin (FUO) remains a persistent diagnostic challenge in clinical medicine despite significant advances in laboratory testing and imaging techniques. The definition of FUO has evolved since the original criteria proposed in 1961 and currently refers to persistent fever exceeding approximately 38.2-38.3 °C without a definitive diagnosis after an adequate diagnostic evaluation. Gastrointestinal diseases represent an important but often underrecognized group of conditions associated with FUO. The aim of this review is to synthesize current evidence on the gastroenterological causes of FUO, with particular emphasis on pathophysiological mechanisms, diagnostic strategies, and therapeutic management. The analysis highlights the role of inflammatory, infectious, and neoplastic gastrointestinal disorders in the etiology of prolonged fever. Key mechanisms involve systemic inflammatory responses mediated by cytokines such as interleukin-1, interleukin-6, and tumor necrosis factor, as well as immune processes associated with the gut-associated lymphoid tissue (GALT) and interactions between intestinal microbiota and host immunity. Among the most frequently reported gastroenterological causes of FUO are inflammatory bowel diseases, intra-abdominal infections and abscesses, hepatobiliary disorders, pancreatitis, and gastrointestinal malignancies. Diagnostic evaluation requires a stepwise approach integrating laboratory testing, microbiological studies, imaging modalities, and endoscopic procedures, with advanced techniques such as computed tomography and fluorodeoxyglucose positron emission tomography improving detection of occult inflammatory or neoplastic processes. Therapeutic management is primarily guided by the identification of the underlying cause, while empirical treatment should be carefully considered to avoid masking diagnostic clues. A better understanding of the gastrointestinal mechanisms underlying FUO and the development of more efficient diagnostic algorithms may improve clinical outcomes and reduce the number of undiagnosed cases.