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

Abstract

Background: Diet and lifestyle-related illnesses like obesity and functional gastrointestinal disorders (FGIDs) are rapidly emerging health issues worldwide. Research has focused on addressing FGIDs via in-person cognitive-behavioral therapies and lifestyle modifications focusing on diet modulation and pharmaceutical intervention. However, there is a paucity of research reporting on the effectiveness of digital care based on genome SNP and gut microbiome markers to guide lifestyle and dietary modulations on FGID associated symptoms and on modeling diseased groups or outcomes based on a combination of these markers. Objective: This study sought to model subjects with FGID symptoms vs. those that do not present them, using demographic, genetic, and baseline microbiome data. Additionally, we aimed at modeling changes in FGID symptom severity of subjects at the time of achieving 5% or more of body weight loss in a digital therapeutics care program compared to baseline symptom severity. Methods: A group of 177 adults with 5% or more weight loss on the Digbi Health personalized digital care program was retrospectively surveyed about changes in the symptomatology of their FGIDs and other comorbidities. The FGID subgroup rated their symptom severity on a scale of 1 to 5 at the beginning of the program and after successfully achieving >5% body weight decrease. During the intervention, personalized coaching for lifestyle changes, including diet and exercise, was delivered by both human and digital coaching. The demographic, genomic, and baseline microbiome data of the subgroup of participants (n=104) who self-reported any of six FGIDs (IBS, diarrhea, constipation, bloating, gassiness, and cramping) were compared with those who did not report FGIDs (n=73) and used as variables for a logistic model. The sum of reductions in symptom severity and IBS, diarrhea, and constipation symptom severity reduction were analyzed using the same variables in linear regression models. Results: Gut microbiome taxa and demographics were the strongest predictors of FGID status. The digital therapeutics program implemented effectively reduced the summative severity of symptoms for 89.92% of users who reported FGIDs, with a highly significant reduction in severity (Wilcoxon signed-rank test, p=4.89e-17*). A mixture of genomic and microbiome predictors modeled the best reduction in summative FGID symptom severity and IBS symptom severity, whereas reduction in diarrhea symptom severity and constipation symptom severity were best modeled by microbiome predictors only. Conclusion: A digital therapeutics program, informed by genomic SNPs and baseline gut microbiome and their interaction with participant diet and lifestyle, can effectively reduce functional bowel disorder symptomatology. While further research is needed for validation, demographics, microbiome taxa, and genetic markers can effectively inform models aiming at classifying subjects with FGIDs vs. those that do not have FGIDs and models assessing the reduction in symptom severity experienced by FGID sufferers. The methods and models presented here can readily be implemented to study other comorbidities where genetics and gut microbiome play a central role in disease etiology.

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

“…Digbi Health’s intervention has been described elsewhere [28]. In a nutshell, personalization of plans is achieved by analyzing participants’ genetics, gut bacteria, lifestyle, and demographics.…”

“…We further obtained the ATC codes (https://www.whocc.no/atc_ddd_index/) from which we identified medications prescribed for depression (N06AA, N06AB, and N06AX), anxiety (N05B) and antibiotics and antimycotics (J01B, J01C, J01D, J01E, J01F, J01G, J01M, J01R, J01X, J02, and J04). Digbi Health's intervention has been described elsewhere [28]. In a nutshell, personalization of plans is achieved by analyzing participants' genetics, gut bacteria, lifestyle, and demographics.…”

“…Digbi Health has implemented and commercialized 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 provide body weight loss in over 70% of individuals [26], reduction of fasting blood glucose level by 17.55% and an average reduction of HbA1c level by 6.27% [27], and a significant reduction in the symptomatology of Functional Gastro-Intestinal Disorders (FGIDs) [28]. This study addresses if gut microbiome taxa or functions and genetic markers explain body weight loss's effect on mental health.…”

Comorbidity of functional bowel disorders and obesity in terms of microbiome

“…c помощью регрессионного анализа пытались определить биомаркеры ФГИР у людей с ожирением или избыточным весом. Согласно результатам исследования, наличие представителей родов Fusicatenibacter, Intestinimonas и Megasphaera связано с уменьшением симптомов ФГИР [42]. Эти бактерии являются продуцентами короткоцепочечных жирных кислот (КЖК) [45][46][47][48].…”

Dietary Fat Effect on the Gut Microbiome, and Its Role in the Modulation of Gastrointestinal Disorders in Children with Autism Spectrum Disorder

“…High levels of H2S may cause mucus disruption and inflammation, while low levels can directly stabilize the mucus layers of the gut, prevent fragmentation and adherence of the microbiota biofilm to the epithelium, inhibit the invasion of pathogenic bacteria, and help resolve inflammation and tissue injury [79]. Gut Desulfovibrio was associated with an increase in the self-reported severity of gastrointestinal symptoms in adults [80], however Desulfovibrio abundance was not found to be correlated to GI symptoms in children with ASD [75]. It was noted that there is a dose response in ASD severity with increased Desulfovibrio, which was hypothesized to be attributed to its production of lipopolysaccharides (LPS) and reduction of sulfate, otherwise required for other physiological reactions [81].…”