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BACKGROUND Inflammatory bowel disease (IBD) flares are common and unpredictable. Disease monitoring relies on symptom reporting or single timepoint assessments of stool, blood, imaging, or endoscopy—these are inconvenient and invasive and do not always reflect the patient perspective. Advances in wearable technology allow for passive, continuous and non-invasive assessment of physiological metrics including heart rate variability (HRV), the measure of small time differences between each heartbeat, a marker of autonomic nervous system function. Our group has previously demonstrated that changes in autonomic function precedes an IBD flare, can predict psychological state transitions and even identify inflammatory events including SARS-CoV-2 infection. To develop algorithms that can predict IBD flares using wearable device signatures, we launched a national wearable device study called The IBD Forecast study. To assess data quality and feasibility, the first 125 Apple Watch users to enroll were evaluated. METHODS The IBD Forecast study is a prospective cohort study enrolling anyone ≥18 years of age in the United States (US) with IBD who is willing to (1) use a commercially available wearable device, (2) download our custom eHive app and (3) answer daily survey questions. HRV metrics (mean of the standard deviations of all the NN intervals [SDNN]) were analyzed using a mixed-effect cosigner model that incorporated body mass index, age, and sex. SDNN is a time domain HRV index that reflects both sympathetic and parasympathetic nervous system activity and is calculated from the variance of intervals between adjacent QRS complexes (the normal-to-normal [NN] intervals). Clinical flare was assessed with daily Patient Reported Outcome (PRO)-2 surveys (flare; PRO-2 Crohn’s disease >7, PRO-2 ulcerative colitis >2). Inflammatory flare was assessed via patient reported C-reactive protein (CRP), with inflammatory flare defined as >5 mg/L. RESULTS The first 125 study participants were enrolled across 29 states in the US (Table 1). Circadian features of changes of HRV were modelled (Figure 1). The mesor, or midline of the circadian pattern of the SDNN was higher in those with clinical flare (mean 44.43; 95% CI 41.25-47.75) compared to those in clinical remission (mean 43.03; 95% CI 39.94-46.22) (p CONCLUSIONS Longitudinally collected HRV metrics from a commonly worn commercial wearable device can identify symptomatic and inflammatory flares. This preliminary analysis of a small proportion of the IBD Forecast Study cohort demonstrates the feasibility of using wearable devices to identify, and may potentially predict, IBD flares.

Summary Fecal IgA production depends on colonization by a gut microbiota. However, the bacterial strains that drive gut IgA production remain largely unknown. Here, we assessed the IgA-inducing capacity of a diverse set of human gut microbial strains by monocolonizing mice with each strain. We identified Bacteroides ovatus as the species that best induced gut IgA production. However, this induction varied bimodally across different B. ovatus strains. The high IgA-inducing B. ovatus strains preferentially elicited more IgA production in the large intestine through the T cell-dependent B cell-activation pathway. Remarkably, a low-IgA phenotype in mice could be robustly and consistently converted into a high-IgA phenotype by transplanting a multiplex cocktail of high IgA-inducing B. ovatus strains but not individual ones. Our results highlight the critical importance of microbial strains in driving phenotype variation in the mucosal immune system and provide a strategy to robustly modify a gut immune phenotype, including IgA production.