High-Dimensional Clustering of 4000 Irritable Bowel Syndrome Patients Reveals Seven Distinct Disease Subsets

BACKGROUND AND AIMS: Irritable bowel syndrome (IBS) is a pain disorder classified by bowel habits, disregarding other factors that may influence the clinical course. The aim of this study was to determine if IBS patients can be clustered based on clinical, dietary, lifestyle, and psychosocial factors. METHODS: Between 2013 and 2020, the Mayo Clinic Biobank surveyed and received 40,291 responses to a questionnaire incorporating Rome III criteria. Factors associated with IBS were determined and latent class analysis, a model-based clustering, was performed on IBS cases. RESULTS: We identified 4021 IBS patients (mean 64 years; 75% women) and 12,063 controls. Using 26 variables separating cases from controls, the optimal clustering revealed 7 latent clusters. These were characterized by perceived health impairment (moderate or severe), psychoneurological factors, and bowel dysfunction (diarrhea or constipation predominance). Health impairment clusters demonstrated more pain, with the severe cluster also having more psychiatric comorbidities. The next 3 clusters had unique enrichment of psychiatric, neurological, or both comorbidities. The bowel dysfunction clusters demonstrated less abdominal pain, with diarrhea cluster most likely to report pain improvement with defecation. The constipation cluster had the highest exercise score and consumption of fruits, vegetables, and alcohol. The distribution of clusters remained similar when Rome IV criteria were applied. Physiologic tests were available on a limited subset (6%), and there were no significant differences between clusters. CONCLUSIONS: In this cohort of older IBS patients, 7 distinct clusters were identified demonstrating varying degrees of gastrointestinal symptoms, comorbidities, dietary, and lifestyle factors. Further research is required to assess whether these unique clusters could be used to direct clinical trials and individualize patient management

Gesture Based Hardware Interface for Rf Lighting Control

This paper represents the design and implementation of a novel interactive hardware which recognizes certain hand gestures and responds in a way of controlling a remote light source. The hardware is made as a “Hand Sensing” which provides 3-dimensional light control for switching the light on/off or dimming to a desired level. Control parameters are calculated by an accelerometer mounted on the hand, which detects positions of a user's palm in a 3D space. Slow palm rotation is translated into commands for the light dimming, whereas specific hand movements control the light switching. RF transmitter connected to the accelerometer sends the current palm and hand coordinates to the remote luminary. The luminary is managed by an RF receiver and a module for the light control which translates the received data into lighting commands