A-358 Comparison of Immunoregulatory miRNA, Human Milk oligosaccharides (HMO’s) and Gut Microbiomes between Infants Fed with Human Breast Milk Versus Infant Formula

Abstract Background Nutrition in early childhood has been shown to play a critical role in the maturation of the immune system and results in considerable influences on long-term health outcomes. Among all available nutritional choices for infant feeding, human breast milk has remained the ideal option. Bioactive molecules found in human breast milk include immune cells, cytokines, growth factors, and miRNAs. MicroRNAs and gut microbiome are major regulators of developing immunity and immune responses to pathogens. A good equilibrium of beneficial microbiome makeup propitiates many other friendly microbes Bifidobacterial and Lactobacilli and few others. In contrast, formula-fed babies possess less diverse gut microbial profiles larger amounts of the putative pathogens, hence an easy prey for infection and immune dysfunctions. Methods Exploratory Data Analysis (EDA) has been done to establish an understanding of the underlying distribution of the key variables of interest and to identify preliminary trends or anomalies in the dataset. The dataset used has categorical and continuous variables like type of feeding, miRNA expression, diversity of gut microbiome, HMO composition and other health attributes. Data is obtained from various clinical studies that analyzed infant health databases concerning infant feeding practices as well as how these influence the development of an early-life microbiome. The microbiome has been analyzed through stool samples and characterized through DNA extraction followed by 16S rRNA gene sequencing which reflects the diversity of microbes. Normalized variables include concentration of miRNA and bacterial abundance and categorical variables like feeding type, HMOs, age in months have been encoded into numerical variables to facilitate easier analysis. Results The study makes use of statistical analysis, machine learning models, and dimensionality reduction techniques to infer which of the features drives the significant variations in the composition of microbiomes. K-means clustering, Principal component Analysis (PCA), and Random Forest feature importance analysis unmask the hidden patterns of microbial regulation. The correlations indicated in the heatmap for miRNA concentration and bacterial abundance is 0.06, meaning more miRNAs indicate a small decrease in bacterial production.Kernel Density Estimation (KDE) is plotted to depict the probability density of bacterial abundance. The distribution indicates a bimodal shape at approximately 30,000 and 70,000 CFU/mL. indicates two major bacterial abundance clusters. Random Forest regression shows cluster membership is the strongest predictor, followed by miRNA concentration and infant weight. Infants with the lowest diversity had the highest median bacterial abundance, at 57,870 CFU/mL, whereas those with moderate diversity had the lowest bacterial abundance, at 45,557 CFU/mL. Conclusion Changes in the concentrations of miRNA between the feeding groups indicate the role of miRNA in immunity regulation and in modulating the microbiome. Prebiotic substances HMOs present in breast milk selectively feed beneficial bacteria while stopping harmful pathogens from binding to the gut lining. Bioactive components missing in infant formula increase the chance of infections, diarrhea, inflammatory condition NEC. The study indicates that miRNA plays an imperative role in microbial regulation, giving new insights into infant nutrition and health interventions, through translational research and personalized baby care. Keywords: miRNA, HMO, gut microbiome, microbiome-driven health interventions