Microbial and Metabolite Signatures of Stress Reactivity in Ulcerative Colitis Patients in Clinical Remission Predict Clinical Flare Risk

BACKGROUND

Stress reactivity (SR) is associated with increased risk of flares in ulcerative colitis (UC) patients. Because both preclinical and clinical data support that stress can influence gut microbiome composition and function, we investigated whether microbiome profiles of SR exist in UC.

METHODS

Ninety-one UC subjects in clinical and biochemical remission were classified into high and low SR groups by questionnaires. Baseline and longitudinal characterization of the intestinal microbiome was performed by 16S rRNA gene sequencing and fecal and plasma global untargeted metabolomics. Microbe, fecal metabolite, and plasma metabolite abundances were analyzed separately to create random forest classifiers for high SR and biomarker-derived SR scores.

RESULTS

High SR reactivity was characterized by altered abundance of fecal microbes, primarily in the Ruminococcaceae and Lachnospiraceae families; fecal metabolites including reduced levels of monoacylglycerols (endocannabinoid-related) and bile acids; and plasma metabolites including increased 4-ethyl phenyl sulfate, 1-arachidonoylglycerol (endocannabinoid), and sphingomyelin. Classifiers generated from baseline microbe, fecal metabolite, and plasma metabolite abundance distinguished high vs low SR with area under the receiver operating characteristic curve of 0.81, 0.83, and 0.91, respectively. Stress reactivity scores derived from these classifiers were significantly associated with flare risk during 6 to 24 months of follow-up, with odds ratios of 3.8, 4.1, and 4.9. Clinical flare and intestinal inflammation did not alter fecal microbial abundances but attenuated fecal and plasma metabolite differences between high and low SR.

CONCLUSIONS

High SR in UC is characterized by microbial signatures that predict clinical flare risk, suggesting that the microbiome may contribute to stress-induced UC flares.

An AI-Powered Clinical Decision Support System to Predict Flares in Rheumatoid Arthritis: A Pilot Study

Treat-to-target (T2T) is a main therapeutic strategy in rheumatology; however, patients and rheumatologists currently have little support in making the best treatment decision. Clinical decision support systems (CDSSs) could offer this support. The aim of this study was to investigate the accuracy, effectiveness, usability, and acceptance of such a CDSS-Rheuma Care Manager (RCM)-including an artificial intelligence (AI)-powered flare risk prediction tool to support the management of rheumatoid arthritis (RA). Longitudinal clinical routine data of RA patients were used to develop and test the RCM. Based on ten real-world patient vignettes, five physicians were asked to assess patients' flare risk, provide a treatment decision, and assess their decision confidence without and with access to the RCM for predicting flare risk. RCM usability and acceptance were assessed using the system usability scale (SUS) and net promoter score (NPS). The flare prediction tool reached a sensitivity of 72%, a specificity of 76%, and an AUROC of 0.80. Perceived flare risk and treatment decisions varied largely between physicians. Having access to the flare risk prediction feature numerically increased decision confidence (3.5/5 to 3.7/5), reduced deviations between physicians and the prediction tool (20% to 12% for half dosage flare prediction), and resulted in more treatment reductions (42% to 50% vs. 20%). RCM usability (SUS) was rated as good (82/100) and was well accepted (mean NPS score 7/10). CDSS usage could support physicians by decreasing assessment deviations and increasing treatment decision confidence.