Microbiome Diversity and Differential Abundances Associated with Gastrointestinal Symptoms, BMI, Immune Markers, and Fecal Short Chain Volatile Fatty AcidProfiles

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The gut microbiota and its metabolites – namely short chain volatile fatty acids (SCVFAs) – interact with the digestive, immune, and nervous systems. States of microbiome dysbiosis are highly associated with obesity and GI symptoms, and profiles of SCVFAs, which serve functions as fuel sources and signaling molecules, mimic this dysbiotic state. This study aimed to further our understanding of associations between bacterial diversity and GI symptoms, BMI, immune markers, and SCVFAs and to identify bacteria differentially abundant with changes to the previously mentioned variables.


Data (measures of GI distress, BMI, immunoglobulins, fecal proximate analysis, SCVFAs, and 16s RNA sequences) was extracted from a study containing non-celiac gluten-sensitive and control participants. QIIME2 was used to process 16s RNA data, analyze quantitative, qualitative, phylogenetic quantitative, and phylogenetic qualitative measures of alpha and beta diversity and to perform an analysis of composition of microbes (ANCOM) for differential abundances data.


Many significant differences were seen, namely in multiple measures of alpha diversity for IgG4 (P < 0.018), propionate (P < 0.014), caproate (P < 0.003), heartburn (P < 0.004), urgent need to defecate (P < 0.027), and feelings of incomplete evacuation (P < 0.024). Statistical significance was seen in multiple measures of beta diversity between normal and overweight (P < 0.01), normal and obese (P < 0.005), and overweight and obese BMI (P < 0.016), IgG4 (P < 0.033), propionate (P < 0.001), increased gas (P < 0.024), and urgent need for defecation (P < 0.026). The ANCOM identified multiple species of bacteria differentially abundant with changes to variables.


Findings suggest differences in both alpha and beta diversity with various GI symptoms, SCVFAs, and BMI. This research supports plans to apply analysis to larger sample sizes to train machine learning classifiers to identify important features of microbial profiles associated with certain SCVFA and markers of health.

Funding Sources

ETSU (CCRHS, Deans's Research Enhancement Award and CPH, Health Sciences Funding, Honors College Summer Research Fellowship) and Shield Nutraceuticals, LLC.