Digital Therapeutics Care Utilizing Genetic and Gut Microbiome Signals for the Management of Functional Gastrointestinal Disorders: Results From a Preliminary Retrospective Study

Abstract

Diet and lifestyle-related illnesses including functional gastrointestinal disorders (FGIDs) and obesity are rapidly emerging health issues worldwide. Research has focused on addressing FGIDs via in-person cognitive-behavioral therapies, diet modulation and pharmaceutical intervention. Yet, there is paucity of research reporting on digital therapeutics care delivering weight loss and reduction of FGID symptom severity, and on modeling FGID status and symptom severity reduction including personalized genomic SNPs and gut microbiome signals. Our aim for this study was to assess how effective a digital therapeutics intervention personalized on genomic SNPs and gut microbiome signals was at reducing symptomatology of FGIDs on individuals that successfully lost body weight. We also aimed at modeling FGID status and FGID symptom severity reduction using demographics, genomic SNPs, and gut microbiome variables. This study sought to train a logistic regression model to differentiate the FGID status of subjects enrolled in a digital therapeutics care program using demographic, genetic, and baseline microbiome data. We also trained linear regression models to ascertain changes in FGID symptom severity of subjects at the time of achieving 5% or more of body weight loss compared to baseline. For this we utilized a cohort of 177 adults who reached 5% or more weight loss on the Digbi Health personalized digital care program, who were retrospectively surveyed about changes in symptom severity of their FGIDs and other comorbidities before and after the program. Gut microbiome taxa and demographics were the strongest predictors of FGID status. The digital therapeutics program implemented, reduced the summative severity of symptoms for 89.42% (93/104) of users who reported FGIDs. Reduction in summative FGID symptom severity and IBS symptom severity were best modeled by a mixture of genomic and microbiome predictors, whereas reduction in diarrhea and constipation symptom severity were best modeled by microbiome predictors only. This preliminary retrospective study generated diagnostic models for FGID status as well as therapeutic models for reduction of FGID symptom severity. Moreover, these therapeutic models generate testable hypotheses for associations of a number of biomarkers in the prognosis of FGIDs symptomatology.

Mental Health Symptom Reduction Using Digital Therapeutics Care Informed by Genomic SNPs and Gut Microbiome Signatures

“…Digbi Health’s intervention has been described elsewhere [ 33 ]. In a nutshell, personalization of dietary plans is achieved by analyzing participants’ genetics, gut bacteria, lifestyle, and demographics.…”

“…Digbi Health has implemented a personalized digital care intervention that uses its multi-omics platform to provide dietary and lifestyle recommendations personalized using genetic and microbiome information. The intervention has been shown to deliver body weight loss in over 70% of individuals [ 31 ], reduction of fasting blood glucose level by 17.55% and an average reduction of HbA1c level by 6.27% [ 32 ], and a significant reduction in the symptomatology of functional gastro-intestinal disorders (FGIDs) [ 33 ]. This study addresses if gut microbiome taxa or functions and genetic markers explain body weight loss’s effect on mental health.…”

“…Figure S1: Study design and cohort sample size for each outcome, Figure S2: Changes in symptom intensity after the intervention, Figure S3: Boxplot of microbiome features associated with improvement on anxiety, Figure S4: Boxplot of genetic scores associated with improvement on anxiety, Figure S5: Boxplot of microbiome features associated with improvement on depression, Figure S6: Boxplot of genetic scores associated with improvement on depression, Figure S7: Boxplot of microbiome features associated with improvement on insomnia, Figure S8: Boxplot of genetic scores associated with improvement on insomnia, Figure S9: Community-level comparison of the gut microbiome of 328 individuals with and without reported anxiety or depression at baseline, Figure S10: Boxplot of genetic scores associated with anxiety or depression at baseline, Figure S11: Boxplot of microbiome features associated with sleep problems at baseline, Figure S12: Comparison of models for all outcomes of the study based on demographic, microbiome and genetic factors, Figure S13: Comparison of functional pathway associations with outcomes at baseline versus improvement at follow-up, Figure S14: Comparison of bacterial genera associations with outcomes at baseline versus improvement at follow-up, Figure S15: Comparison of genetic scores associations with outcomes at baseline versus improvement at follow-up. Table S1: Demographic data of participants and survey information used in the study, Table S2: Summary characteristics of the cohort, Table S3: Genetic scores used in the study, Table S4: Summary characteristics of the cohorts studied for the improvement of anxiety, depression, and insomnia, Table S5: Summary statistics of genetic and microbiome factors significantly associated with the study outcomes, Table S6: Statistical associations with improvement in anxiety, Table S7: Statistical associations with improvement in depression, Table S8: Statistical associations with improvement in insomnia, Table S9: Summary characteristics of the cohorts studied at baseline, Table S10: Statistical associations with anxiety or depression at baseline, Table S11: Statistical associations with sleep problems at baseline, Table S12: Model fit of combined models of demographics, microbiome, and genetic predictors, Table S13: Effect of medications on the gut microbiome [ 16 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ,…”

Microbiota in Autism Spectrum Disorder: A Systematic Review

“…From other genera, Clostridiales, elevated in ASD patients especially with FGID symptoms, were observed to correlate with proinflammatory cytokines in the gut mucosa [66]. Moreover, Firmicutes, especially Ruminococcaeae, were found to be more abundant in patients with non-celiac gluten sensitivity [67], while Fusobacterium and Desulfovibrio were linked to FGID symptoms in two other studies [68,69]. The severity of the core symptoms correlated with higher Bacteroides and Desulfovibrio levels, observed also in the context of FGID.…”

Multi-omics for biomarker approaches in the diagnostic evaluation and management of abdominal pain and irritable bowel syndrome: what lies ahead

“…Indeed, some have begun combining multi-omics with digital therapeutics to demonstrate that changes in IBS symptom severity can be modeled through a mixture of genomic and microbiome predictors, suggesting that prognostic multi-omics biomarkers for IBS symptomology are already on the horizon. 164 …”