Hormonal Influences on Inflammatory Bowel Diseases

Of the 1.8 million females with inflammatory bowel diseases (IBD) in the US, over half of those who are premenopausal suffer from cyclical menstrual-related IBD symptoms, regardless of how well their disease is controlled. Despite the significant impact that cyclical IBD symptoms, such as abdominal pain, diarrhea, and fatigue have on quality of life, evidence about how to alleviate these symptoms is lacking. In other chronic conditions which are hormonally influenced, such as epilepsy, hormonal contraception may be used to favorably impact disease-related symptoms associated with menses and improve quality of life. In our previous cross-sectional study, 47% of the levonorgestrel intrauterine device users and 19% of combination oral contraceptive users reported improvement in their cyclical IBD symptom. All hormonal methods may plausibly improve symptoms, but prospective, rigorous data evaluating their efficacy for this purpose are lacking. In order to design a future comparative effectiveness trial on the effect of hormonal contraceptive methods on menstrual-related IBD symptoms, we propose this pilot prospective cohort study of 200 females with IBD: 100 naturally cycling and 100 hormonal contraception users. We will gain essential knowledge on IBD-specific influences on contraceptive method selection, willingness to be randomized to methods, the ability of IBD patient reported outcome (PRO) instruments to differentiate between non-menstrual and menstrual-related IBD symptoms, and assess the potential role of inflammatory markers as outcome measures in future trials. We will recruit participants from the University of Utah IBD Center and clinics, other Utah gastroenterology providers, and through social media ads. Total study commitment will be \~12 weeks. Study activities will include daily and weekly text message surveys, as well as blood draws and fecal samples for inflammatory markers in a subset of participants which are commonly used for IBD management. Our ai