Reprogramming the Human Gut Microbiome Reduces Dietary Energy Harvest

The gut microbiome is emerging as a key modulator of host energy balance1. We conducted a quantitative bioenergetics study aimed at understanding microbial and host factors contributing to energy balance. We used a Microbiome Enhancer Diet (MBD) to reprogram the gut microbiome by delivering more dietary substrates to the colon and randomized healthy participants into a within-subject crossover study with a Western Diet (WD) as a comparator. In a metabolic ward where the environment was strictly controlled, we measured energy intake, energy expenditure, and energy output (fecal, urinary, and methane)2. The primary endpoint was the within-participant difference in host metabolizable energy between experimental conditions. The MBD led to an additional 116 ± 56 kcals lost in feces daily and thus, lower metabolizable energy for the host by channeling more energy to the colon and microbes. The MBD drove significant shifts in microbial biomass, community structure, and fermentation, with parallel alterations to the host enteroendocrine system and without altering appetite or energy expenditure. Host metabolizable energy on the MBD had quantitatively significant interindividual variability, which was associated with differences in the composition of the gut microbiota experimentally and colonic transit time and short-chain fatty acid absorption in silico. Our results provide key insights into how a diet designed to optimize the gut microbiome lowers host metabolizable energy in healthy humans.

[1]  B. Rittmann,et al.  Developing a model for estimating the activity of colonic microbes after intestinal surgeries , 2021, PloS one.

[2]  Steven R Smith,et al.  Energy intake as a short‐term biomarker for weight loss in adults with obesity receiving liraglutide: A randomized trial , 2021, Obesity science & practice.

[3]  Timothy L. Tickle,et al.  Multivariable association discovery in population-scale meta-omics studies , 2021, bioRxiv.

[4]  P. Manghi,et al.  Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3 , 2020, bioRxiv.

[5]  B. Rittmann,et al.  Chemical Oxygen Demand Can Be Converted to Gross Energy for Food Items Using a Linear Regression Model. , 2020, The Journal of nutrition.

[6]  Kong Y Chen,et al.  Room Indirect Calorimetry Operating and Reporting Standards (RICORS 1.0): A Guide to Conducting and Reporting Human Whole‐Room Calorimeter Studies , 2020, Obesity.

[7]  C. Champagne,et al.  Integrative and quantitative bioenergetics: Design of a study to assess the impact of the gut microbiome on host energy balance , 2020, Contemporary clinical trials communications.

[8]  Roland Eils,et al.  Complex heatmaps reveal patterns and correlations in multidimensional genomic data , 2016, Bioinform..

[9]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[10]  Hyun Kang The prevention and handling of the missing data , 2013, Korean journal of anesthesiology.

[11]  Thomas R. Gingeras,et al.  STAR: ultrafast universal RNA-seq aligner , 2013, Bioinform..

[12]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[13]  J. Burgess,et al.  Improvement of phylum- and class-specific primers for real-time PCR quantification of bacterial taxa. , 2011, Journal of microbiological methods.

[14]  M. Sadílek,et al.  Detection of Polyethylene Glycol–based Laxatives in Stool , 2010, Journal of pediatric gastroenterology and nutrition.

[15]  J. S. Douglass,et al.  Resistant starch intakes in the United States. , 2008, Journal of the American Dietetic Association.

[16]  J. Rood,et al.  Hormonal Responses to a Fast-Food Meal Compared with Nutritionally Comparable Meals of Different Composition , 2007, Annals of Nutrition and Metabolism.

[17]  S. Lewis,et al.  Stool form scale as a useful guide to intestinal transit time. , 1997, Scandinavian journal of gastroenterology.

[18]  D. Altman,et al.  STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT , 1986, The Lancet.