Patient-reported exposures and outcomes link the gut-brain axis and inflammatory pathways to specific symptoms of severe mental illness.

We developed a "gut-brain-axis questionnaire" (GBAQ) to obtain standardized person-specific "review of systems" data for microbiome-gut-brain-axis studies. Individual items were compared to PANSS symptom measures using dimensional, transdiagnostic and traditional categorical approaches. METHOD: Forty psychotic participants, independent of diagnoses, and 42 without psychosis (18 nonpsychotic affective disorders, 24 healthy controls) completed the GBAQ and underwent research diagnostic and symptom assessments. The PANSS scales and its dysphoric mood, autistic preoccupation and activation factors were computed. RESULTS: Transdiagnostic analyses robustly linked psychosis severity to constipation (p<.001), and Negative (p=.045) and General Psychopathology scores (p=.016) with bowel hypomotility. Activation factor scores predicted numbers of psychiatric (p=.009) and medical conditions (p=.003), BMI (p=.003), skin (p<.001) and other conditions. Categorical analyses comparing psychotic, nonpsychotic and control groups revealed behavioral differences: cigarette smoking (p=.013), alcohol use (p=.007), diet (p's <.05), exercise (p<.001). All subjects accurately self-reported their diagnosis. CONCLUSIONS: The GBAQ is a promising tool. Transdiagnostic analyses associated psychotic symptoms to gut hypomotility, indicative of low gut vagal tone, consistent with reduced cardiovagal activity in psychosis. Activation, similar to delirium symptoms, predicted medical comorbidity and systemic inflammatory conditions. Group level comparisons only showed behavioral differences. Underpinnings of psychiatric disorders may include reduced gut vagal function, producing psychosis, and systemic inflammation, impacting risks for psychotic and nonpsychotic conditions.

Association between behavioral phenotypes and sustained use of smartphones and wearable devices to remotely monitor physical activity.

Smartphones and wearable devices can be used to remotely monitor health behaviors, but little is known about how individual characteristics influence sustained use of these devices. Leveraging data on baseline activity levels and demographic, behavioral, and psychosocial traits, we used latent class analysis to identify behavioral phenotypes among participants randomized to track physical activity using a smartphone or wearable device for 6 months following hospital discharge. Four phenotypes were identified: (1) more agreeable and conscientious; (2) more active, social, and motivated; (3) more risk-taking and less supported; and (4) less active, social, and risk-taking. We found that duration and consistency of device use differed by phenotype for wearables, but not smartphones. Additionally, "at-risk" phenotypes 3 and 4 were more likely to discontinue use of a wearable device than a smartphone, while activity monitoring in phenotypes 1 and 2 did not differ by device type. These findings could help to better target remote-monitoring interventions for hospitalized patients.