(P08-014-25) DNA Metabarcoding as a Tool for Dietary Intake Analysis in Inflammatory Bowel Disease

Objectives: Diet can play an important role in inflammatory bowel diseases (IBD). However, dietary data are often unrecorded or collected using self-report methods, which are limited and subject to misreporting, contributing to the low quality of evidence for dietary guidelines in IBD. To address these challenges, we refined our DNA metabarcoding method, FoodSeq, to amplify both plant- and animal-based food DNA from human stool to reconstruct dietary intake. Our objective was to validate this updated method and test its feasibility in the context of IBD.

Methods: We modified FoodSeq by adding animal-specific primers (12SV5) alongside plant-specific primers (trnL), for multiplexed sequencing of both gene markers. We then performed FoodSeq on DNA extracted from stool samples from 39 individuals with and without IBD, before and after gastrointestinal surgery. We also performed FoodSeq on longitudinal samples from four individuals without IBD undergoing a dietary intervention, to further evaluate our protocol. We performed principal component analysis (PCA), permutational multivariate analysis of variance (PERMANOVA), and linear mixed effects models to examine the dietary species composition of our samples.

Results: We observed a dietary shift following surgery in individuals with IBD, but not in those without IBD. Among the IBD samples, principal component analysis revealed a significant difference between pre- and post-operative samples along the first principal component (PC1) (p = 0.0119). Pre-operative samples clustered positively along PC1, whereas post-operative samples clustered negatively. The top five positive contributors to PC1 were banana, strawberry, oat, rice, and chicken, while the top five negative contributors were cattle, sesame, the rye/wheat family, hemp seed, and the guar/soybean family.

Conclusions: We detected dietary patterns using FoodSeq in an IBD cohort without previously recorded dietary data, demonstrating the potential of this tool to capture diet-related outcomes in other cohorts with biobanked stool.

Funding Sources: This research was supported by NIH T32 and R01 awards.