Background: Self-directed lifestyle modifications are essential for managing symptoms in individuals diagnosed with irritable bowel syndrome (IBS). This study incorporated longitudinal multi-omics profiling to estimate the mechanisms underlying responses to a nurse-led person-centered self-management intervention in young adults with IBS. Methods: This pre-post study was nested within a 12-week parent randomized controlled trial (NCT03332537). Biospecimens (stool and blood) and clinical outcomes were collected at baseline and post-intervention. Symptoms were assessed using the Brief Pain Inventory and PROMIS^® short forms. Host transcriptomic profiling was performed using RNA sequencing, and gut microbial composition was analyzed via 16S rRNA sequencing. Host transcriptomic co-expression and microbial co-abundance modules were identified via weighted gene co-expression network analysis. Associations between multi-omics modules and symptoms were evaluated using linear mixed-effect models. Results: Among the 20 participants, most were non-Hispanic (75%), White (75%), and female (65%). The intervention significantly reduced self-reported pain severity (p = 0.019) and pain interference (p = 0.013). Decreased associations were observed between pain phenotypes and a microbial module enriched in core metabolic pathways (interference: β = −4.7, p < 0.001; severity: β = −2.4, p = 0.02). Anxiety strengthened associations with host transcriptomic cellular energy metabolism pathways post-intervention (p < 0.05). The intervention attenuated associations between fatigue, sleep disturbance, and immune–inflammatory transcriptomic and microbial adaptation modules (p < 0.05). Conclusions: Findings suggest that the IBS self-management intervention induces symptom-specific biological responses, implicating distinct host–microbe pathways. Larger longitudinal studies are warranted to validate these omics-based symptom signatures.

Background: Women from diverse socioeconomic status (SES) face a higher risk of preterm birth, increasing their infants’ vulnerability to neurodevelopmental and other health disorders; however, the predictive role of maternal ZIP code level SES in these outcomes remains underexplored. Objectives: To investigate the associations between maternal racial disparity, ZIP code-level SES, and infant breastfeeding, growth, and neurodevelopmental trajectories. Methods: In this cohort study, preterm infants were recruited from two Connecticut neonatal intensive care units (NICUs). Infant demographic data, feeding regimens, and growth during the NICU stay were documented. Neurodevelopmental outcomes were assessed using the NICU Neonatal Neurobehavioral Scale, the Bayley scale of infant and toddler development (3rd ed.), and the Brief Infant Toddler Social Emotional Assessment. To compare SES differences between infants born to Black and White mothers, both t -tests and Wilcoxon tests were conducted. We used XGBoost to analyze infant health outcomes and SHapley Additive exPlanations (SHAP) values to identify SES-related risk factors associated with feeding, growth during NICU stay, and neurodevelopmental outcomes up to 2 years of age. Results: In total, 181 preterm infants from eight ZIP code areas were included in the study. The majority of infants were born to mothers who were White and non-Hispanic. Compared with infants born to White mothers, those born to Black mothers had younger birth gestational age (GA), lower birth weights, shorter birth lengths, smaller head circumferences, and higher Score of Neonatal Acute Physiology with Perinatal Extension–II (SNAPPE–II), with all differences being statistically significant. Compared with White mothers, Black mothers were younger, single, and less educated. Black mothers also had lower median household incomes, larger average family sizes, and higher levels of poverty compared with White mothers. Based on SHAP values, the risk factors predicting infants’ feeding, growth, and neurodevelopment are ranked as follows: birth weight, birth GA, SNAPPE–II score, average family size, maternal age, median household income, poverty level, and school enrollment. Discussion: Maternal racial disparity and SES serve as predictors of feeding, growth, and neurodevelopmental outcomes in preterm infants. Understanding these associations can inform health care strategies for vulnerable preterm populations to improve long-term health outcomes.

Wearable devices for continuous health monitoring in humans are constantly evolving, yet the signal quality may be improved by optimizing electrode placement. While the commonly used locations to measure electrodermal activity (EDA) are at the fingers or the wrist, alternative locations, such as the torso, need to be considered when applying an integrated multimodal approach of concurrently recording multiple bio-signals, such as the monitoring of visceral pain symptoms like those related to irritable bowel syndrome (IBS). This study aims to quantitatively determine the EDA signal quality at four torso locations (mid-chest, upper abdomen, lower back, and mid-back) in comparison to EDA signals recorded from the fingers. Concurrent EDA signals from five body locations were collected from twenty healthy participants as they completed a Stroop Task and a Cold Pressor task that elicited salient autonomic responses. Mean skin conductance (meanSCL), non-specific skin conductance responses (NS.SCRs), and sympathetic response (TVSymp) were derived from the torso EDA signals and compared with signals from the fingers. Notably, TVSymp recorded from the mid-chest location showed significant changes between baseline and Stroop phase, consistent with the TVSymp recorded from the fingers. A high correlation (0.77–0.83) was also identified between TVSymp recorded from the fingers and three torso locations: mid-chest, upper abdomen, and lower back locations. While the fingertips remain the optimal site for EDA measurement, the mid-chest exhibited the strongest potential as an alternative recording site, with the upper abdomen and lower back also demonstrating promising results. These findings suggest that torso-based EDA measurements have the potential to provide reliable measurement of sympathetic neural activities and may be incorporated into a wearable belt system for multimodal monitoring.

Introduction: Irritable bowel syndrome (IBS) is a prevalent disorder whose most debilitating symptom is pain. The complex, multifactorial nature of IBS pain leads to highly variable and often inadequate responses to self-management, underscoring the urgent need for personalized prediction models. Methods: This ancillary analysis of a randomized controlled trial ( NCT03332537 ) utilized data from 80 young adults with IBS. We applied the Bayesian Additive Regression Trees machine learning algorithm to develop 27 distinct predictive models for pain severity, pain interference, and quality of life (QOL) at baseline and post-intervention. Predictors included a comprehensive, multi-domain set of variables spanning genetics, quantitative sensory testing, gut microbiota, psychosocial factors, and food intake. Results: Model performance was strong, with area under the curve (AUC) values ranging from 0.753 to 0.981. A consistent hierarchy of predictors emerged. The COMT rs4680 polymorphism was the most significant predictor, featuring in 26 models, followed by ADRA1D rs1556832 in 24 models. The mechanical pain threshold was a key predictor of pain severity, while psychosocial factors, particularly pain catastrophizing, were crucial for pain interference and QOL. Gut microbiota features and food intake were also consistently important. Discussion: This study establishes a comprehensive, multi-omics framework that explains individual differences in IBS pain and treatment response. The identified predictors provide a practical tool for advancing precision medicine. By classifying patients based on their distinct profiles, clinicians can proactively customize self-management strategies, potentially transforming care for this complex condition. WHAT IS KNOWN: IBS pain involves complex, dysregulated gut-brain interactions. Treatment response is highly variable and difficult to predict. Machine learning can help phenotype complex pain conditions. WHAT IS NEW HERE: Machine learning based hierarchical models identify a core neurogenetic predisposition for IBS pain. Machine learning based prediction of individual response to self-management before treatment begins. This provides a direct pathway to precision pain management in IBS.

Background: Heterogeneity in symptom presentation and treatment response in irritable bowel syndrome (IBS) remains poorly understood. The gut microbiota may contribute to this variability, but its role in shaping symptom trajectories and responses to self-management interventions is unclear. Objective: To identify symptom trajectory phenotypes and determine whether gut microbiota composition and function distinguish these phenotypes and predict multidimensional responses to pain self-management interventions in young adults with IBS. Design: Ancillary data analysis from a randomized control trial ( NCT03332537 ). Methods: Participants with longitudinal data (n = 62) were analyzed using longitudinal k-means clustering (KML) based on trajectories of measures in IBS quality of life (QOL), Brief Pain Inventory (BPI), and psychoneurological outcomes (anxiety, applied cognition, depression, fatigue, global health, positive affect, and sleep disturbance) over 12 weeks. Baseline differences between clusters were assessed with Wilcoxon rank-sum tests, and longitudinal changes were evaluated with linear mixed models. Gut microbiota composition and predicted functional pathways were compared between phenotypes. Bayesian Additive Regression Trees (BART) models were used to identify baseline microbial taxa and pathways predictive of longitudinal changes in QOL, BPI pain interference, and severity. Results: Two distinct trajectory-defined response phenotypes were identified: a Constrained Response Phenotype (Phenotype A, n = 35) and an Adaptive Multidomain Response Phenotype (Phenotype B, n = 27). At baseline, Phenotype B showed lower pain severity and interference, but higher levels of anxiety, depression, and fatigue compared to Phenotype A. Over 12 weeks, both phenotypes showed improvements in pain outcomes (all p < 0.05), but only Phenotype B demonstrated broad improvements across psychoneurological domains and QOL (all p < 0.05). Phenotype A exhibited more limited improvements and worsening in several psychoneurological domains. Gut microbiota functional pathways differed between phenotypes, including pathways related to xenobiotic degradation, amino acid metabolism, bile secretion, and immune-related processes (all raw p < 0.05), although these did not remain significant after multiple testing correction. Machine learning models identified distinct, phenotype-specific microbial predictors of intervention response. In Phenotype A, genera such as Alistipes and Sutterella were consistently identified across models, whereas in Phenotype B, predictors included Phascolarctobacterium , Collinsella , and Parabacteroides . Functional pathways also differed between phenotypes, suggesting distinct microbiome-linked mechanisms underlying symptom trajectories and responses to pain interventions. Conclusions: Young adults with IBS exhibit distinct multidimensional response phenotypes that are associated with differential clinical and microbiome profiles. Baseline gut microbiota composition and functional capacity demonstrate phenotype-specific predictive signatures of treatment response, supporting a microbiome-informed framework for stratifying patients and advancing personalized self-management strategies in IBS. WHAT IS KNOWN: □ Substantial heterogeneity exists in irritable bowel syndrome symptoms and treatment response, with variability across pain, psychological distress, and quality of life domains. □ Gut microbiota composition and function are linked to IBS pathophysiology, including associations with pain sensitivity, inflammation, and brain–gut signaling. □ Self-management interventions (e.g., mindfulness, behavioral strategies) can improve IBS symptoms, but responses are inconsistent and difficult to predict. WHAT IS NEW HERE: □ Distinct longitudinal symptom trajectory phenotypes were identified, separating individuals with pain-predominant and limited psychoneurological improvement from those with lower pain and greater multidomain psychoneurological improvement. □ Gut microbiota composition and functional profiles varied between trajectory-defined clusters, indicating a biological foundation for differences in symptom patterns. □ Machine learning models showed that gut microbiota features predict pain severity, interference, quality of life, and their longitudinal changes, supporting microbiome-based stratification for self-management outcomes.