Network-based representation learning reveals the impact of time and diet on the gut microbial and metabolomic environment of infants
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While studies have explored differences in gut microbiome development regarding infant liquid diets (breastmilk, formula), surprisingly little is known about the impact of complementary foods on the infant gut microbiome. Indeed, current dietary recommendations for infants and toddlers are not formulated with knowledge of how the developing gut microbiome metabolizes complementary foods. Here, we investigated how different protein-rich foods (i.e., meat vs. dairy) affect fecal metagenomics and metabolomics during early complementary feeding from 5-12 months in formula-fed infants. We used a network node embedding to model the time-dependent, complex interactions between microbiome features, metabolomic compound features, and diet. We then used the embedded space to detect features associated with baseline or endpoint and meat or dairy diet-finding enriched networks of microbiome and metabolomic features, and compared the results to those found using a more traditional differential abundance analysis.
