Association Between Gut Microbiome Diversity and Severe Acute Malnutrition in Children Under Five Years

Abstract

Background: Severe acute malnutrition (SAM) remains one of the leading causes of morbidity and mortality among children under five years of age, particularly in low- and middle-income countries. Recent advances in microbiome research have highlighted the critical role of gut microbial diversity in nutrient metabolism, immune regulation, intestinal barrier integrity, and overall child growth. However, the complex interactions between gut microbiota and nutritional status are difficult to investigate through conventional clinical studies because of ethical, financial, and logistical constraints. Computational simulation offers an alternative framework for exploring these relationships under controlled conditions. Objective: This study aimed to develop a Monte Carlo simulation-based predictive model to investigate the anticipated association between gut microbiome diversity and severe acute malnutrition in children under five years of age under varying biological and environmental scenarios. Methods: A probabilistic Monte Carlo simulation comprising 10,000 iterations was designed using distributions informed by published evidence on pediatric gut microbiome diversity, dietary diversity, infection burden, sanitation status, and anthropometric indicators. The simulation incorporated stochastic variability to estimate the probability of severe acute malnutrition under hypothetical conditions. Logistic regression and sensitivity analyses were employed to identify variables exerting the greatest influence on predicted malnutrition risk. Results: The simulation predicted an inverse relationship between gut microbiome diversity and the probability of severe acute malnutrition. Lower simulated Shannon diversity indices and microbial richness were consistently associated with increased predicted malnutrition risk. Greater pathogen abundance and recurrent infections substantially increased the estimated probability of severe acute malnutrition, whereas higher dietary diversity and beneficial bacterial abundance demonstrated protective effects. Sensitivity analysis identified microbial diversity as the most influential predictor within the simulation framework. Conclusion: The proposed Monte Carlo simulation suggests that reduced gut microbiome diversity may substantially increase the predicted risk of severe acute malnutrition among children under five years of age. Although the findings represent computational predictions rather than clinical observations, the developed framework provides a useful tool for hypothesis generation and future validation using longitudinal microbiome datasets.