Modeling the microbial contribution to human Energy Balance using the Digestion, Absorption, and Microbial Metabolism (DAMM) model

Abstract

Abstract Colonic microorganisms have been linked to human health and disease, specifically metabolic disease states such as obesity, but causal relationships remain to be established. Previous work demonstrated that interactions between the host’s diet and intestinal microbiome were associated with human energy balance by affecting the human’s energy absorption, quantified by metabolizable energy. We developed the Digestion, Absorption and Microbial Metabolism (DAMM) model, which explicitly accounts for the energy contributions of the colonic microbial community by: 1) breaking down the diet composition into the gross energy of the individual macronutrients, 2) calculating direct absorption in the upper gastrointestinal tract, 3) using microbial stoichiometry to estimate the consumption of the remaining unabsorbed nutrients by microbes in the large intestine, 4) quantifying predicted production of microbial products (short-chain fatty acids (SCFA) and methane) in the colon, and 5) estimating absorption from the colonic tract to the host. When used to predict the results from a clinical study that compared two distinctly different diets, the DAMM model captured the directionality and magnitude of change in measured metabolizable chemical oxygen demand (which can be converted to metabolizable energy), improved on the accuracy of predictions compared to the Atwater factors by reducing systematic bias on one of the diets, and estimated substrate availability within the colon and rate of production of microbially derived short-chain fatty acids. Measured methane concentrations, combined with findings from the DAMM model, support the hypothesis that methanogens accumulated in mucosal biofilms in participants harboring methanogens. Model outputs also support that colonic transit time directly influenced SCFA absorption rates. The DAMM model now can be linked to existing human models that predict changes in body energy stores to extend our understanding of how microbial metabolic processes affect macronutrient absorption and metabolizable energy.

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Identifiers

DOI

10.1101/2025.01.10.25320296

Journal

medRxiv

Year

2025