Dynamics of Disease Activity in Monitoring Therapeutic Outcomes and Predicting Prognosis for Ulcerative Colitis

Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease (IBD) with an unknown etiology (cause), with symptoms such as bloody diarrhea, frequent defecation, and tenesmus (a feeling of needing to go to the toilet even though the bowels are empty). In 2023, UC was estimated to affect as many as 5 million people, thereby making UC a disease of global concern. Typically occurring in adolescents and adults, UC usually requires lifelong management, significantly affecting patients' quality of life and placing a heavy burden on families, society, and healthcare systems.

In recent years there has been a surge in the development of targeted therapies for UC which specifically target the immune system of the gut, rather than the more traditional non-targeted treatments which affected the whole body. However, although targeted therapies, or biologics, have improved the expected outcomes for UC patients, 30-50% of patients may still encounter poor outcomes, therefore, timely monitoring of disease activity and prediction of therapeutic response are vital processes during the initial year of treatment.

Describing the trajectory of the course of a disease (the path it follows) is a promising topic in current clinical research. Recently, trajectories of clinical symptoms, psychological evaluations, health-related quality of life and biomarkers (molecules found in blood, other body fluids, or tissues that are a sign of a condition or disease) were explored in newly diagnosed UC patients, helping to identify some relationships between these factors and the course of the disease.

Fecal lactoferrin is a protein released by the body's immune system, and the amount released has been shown to correlate with the severity of inflammation in the gut. One of our previous studies has observed that the trajectory of fecal lactoferrin when a UC patient started biologic therapy was associated with symptom remission after one year. In this way, use of trajectories could be a promising method for disease course monitoring and prognosis, while relatively limited studies towards this have been reported in UC patients with biologic treatments.

Therefore, in order to investigate the role of disease activity trajectories in monitoring the effectiveness of medications and predicting prognosis, this study will analyze the patient level data during biologics therapy from five randomized clinical trials, including UNIFI, PURSUIT, ACT 1, GEMINI 1 and VARSITY.