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

Journal
Frontiers in Microbiology
ISSN
1664-302X
Date Issued
2022
Author(s)
Shreyas V. Kumbhare
Patricia A. Francis-Lyon
Dashyanng Kachru
Tejaswini Uday
Carmel Irudayanathan
Karthik M. Muthukumar
Roshni R. Ricchetti
Simitha Singh-Rambiritch
Juan Ugalde
Parambir S. Dulai
Daniel E. Almonacid
Ranjan Sinha
Type
Resource Types::text::journal::journal article
Scopus ID
2-s2.0-85128162358
WoS ID
WOS:000779721200001
DOI
URL
URL Institutional Repository
Abstract
<jats:p>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.</jats:p>
Cite this document
Kumbhare, S. V., Francis-Lyon, P. A., Kachru, D., Uday, T., Irudayanathan, C., Muthukumar, K. M., Ricchetti, R. R., Singh-Rambiritch, S., Ugalde, J., Dulai, P. S., Almonacid, D. E., & Sinha, R. (2022). Digital therapeutics care utilizing genetic and gut microbiome signals for the management of functional gastrointestinal disorders: Results from a preliminary retrospective study. Frontiers in Microbiology, 13, 826916. https://doi.org/10.3389/fmicb.2022.826916
Dataset(s)
Subjects
multi-omic models

; 

functional gastrointestinal disorders (fgids)

; 

ibs

; 

irritable bowel syndrome

; 

diarrhea

; 

constipation

; 

digital therapeutics

; 

non-pharmacological treatment

; 

cannabinoid

; 

insulin

; 

interleukin 10

; 

inulin

; 

rna 16s

; 

tumor necrosis factor

; 

abdominal cramp

; 

abdominal pain

; 

adult

; 

akkermansia

; 

alcohol consumption

; 

anaerostipe

; 

article

; 

bloating

; 

body mass

; 

body weight

; 

body weight loss

; 

buccal swab

; 

cohort analysis

; 

comorbidity

; 

constipation

; 

controlled study

; 

diarrhea

; 

diet

; 

dietary intake

; 

disease severity

; 

dna extraction

; 

escherichia

; 

exercise

; 

false discovery rate

; 

feces analysis

; 

feces culture

; 

female

; 

food intake

; 

fusicatenibacter

; 

gastrointestinal disease

; 

genotyping

; 

haemophilus

; 

holdemanella

; 

human

; 

inflammation

; 

inflammatory bowel disease

; 

insulin resistance

; 

insulin sensitivity

; 

intestine flora

; 

lifestyle

; 

lifestyle modification

; 

linear regression analysis

; 

major clinical study

; 

male

; 

megasphaera

; 

microbiome

; 

middle aged

; 

nanoarchaeota

; 

nonhuman

; 

normal human

; 

nutrient

; 

obesity

; 

organisms

; 

parabacteroides

; 

questionnaire

; 

retrospective study

; 

ruminococcus torque

; 

shigella

; 

single nucleotide polymorphism

; 

single nucleotide polymorphism array

; 

sleep quality

; 

smoking

; 

terrisporobacter