Chemical Oxygen Demand Can Be Converted to Gross Energy for Food Items Using a Linear Regression Model

Targeting the Gut Microbiota's Role in Host Energy Absorption With Precision Nutrition Interventions for the Prevention and Treatment of Obesity

The field of precision nutrition aims to develop dietary approaches based on individual biological factors such as genomics or the gut microbiota. The gut microbiota, which is the highly individualized and complex community of microbes residing in the colon, is a key contributor to human physiology. Although gut microbes play multiple roles in the metabolism of nutrients, their role in modulating the absorption of dietary energy from foods that escape digestion in the small intestine has the potential to variably affect energy balance and, thus, body weight. The fate of this energy, and its subsequent impact on body weight, is well described in rodents and is emerging in humans. This narrative review is focused on recent clinical evidence of the role of the gut microbiota in human energy balance, specifically its impact on energy available to the human host. Despite recent progress, remaining gaps in knowledge present opportunities for developing and implementing strategies to understand causal microbial mechanisms related to energy balance. We propose that implementing rigorous microbiota-focused measurements in the context of innovative clinical trial designs will elucidate integrated diet-host-gut microbiota mechanisms. These mechanisms are primed to be targets for precision nutrition interventions to optimize energy balance to achieve desired weight outcomes. Given the magnitude and impact of the obesity epidemic, implementing these interventions within comprehensive weight management paradigms has the potential to be of public health significance.

Global metabolite profiling in feces, serum, and urine yields insights into energy balance phenotypes induced by diet-driven microbiome remodeling

Background

Preclinical literature and behavioral human data suggest that diet profoundly impacts the human gut microbiome and energy absorption-a key determinant of energy balance. To determine whether these associations are causal, domiciled controlled feeding studies with precise measurements of dietary intake and energy balance are needed. Metabolomics-a functional readout of microbiome modulation-can help identify putative mechanisms mediating these effects. We previously demonstrated that a high-fiber, minimally processed Microbiome Enhancer Diet (MBD) fed at energy balance decreased energy absorption and increased microbial biomass relative to a calorie-matched fiber-poor, highly processed Western Diet (WD).

Objective

To identify metabolic signatures distinguishing MBD from WD feeding and potential metabolomic mechanisms mediating the MBD-induced negative energy balance.

Methods

We deployed global metabolomics in feces, serum, and urine using samples collected at the end of a randomized crossover controlled feeding trial delivering 22 days of an MBD and a WD to 17 persons without obesity. Samples were collected while participants were domiciled on a metabolic ward and analyzed using Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy. Linear mixed effects models tested metabolite changes by diet. Weighted gene network correlation analysis identified metabolite modules correlated with energy balance phenotypes.

Results

Numerous metabolites consistently altered in the feces, fasting serum, and/or urine may serve as putative dietary biomarkers of MBD feeding. Fecal diet-microbiota co-metabolites decreased by an MBD correlated with reduced energy absorption and increased microbial biomass. An MBD shifted the urinary metabolome from sugar degradation to ketogenesis-evidence of negative energy balance.

Conclusions

Precisely controlled diets disparate in microbiota-accessible substrates led to distinct metabolomic signatures in feces, fasting serum, and/or urine. These diet-microbiota co-metabolites may be biomarkers of a "fed" (MBD) or "starved" (WD) gut microbiota associated with energy balance. These findings lay the foundation for unveiling causal pathways linking diet-microbiota co-metabolism to energy absorption.

Assessing human internal exposure to chemicals at different physical activity levels: A physiologically based kinetic (PBK) model incorporating metabolic equivalent of task (MET)

Physical activity levels have the potential to impact human internal exposure to environmental chemicals. However, the current lack of simple modeling approaches hinders the high-throughput screening of chemical exposure at different physical activity levels. To address this gap, this study proposes a straightforward model for assessing human internal exposure to chemicals. Our approach is based on the physiologically based kinetic (PBK) model and utilizes the metabolic equivalent of task (MET) to characterize internal exposure to chemicals at varying activity levels. To facilitate the application of this model, we have developed an Excel-based operation tool, allowing users to easily modify the MET value and generate simulation results for different physical activity levels. The simulation results demonstrate that as physical activity levels increase, the biotransfer factors (BTFs) of chemicals decrease, suggesting that higher physical activity levels reduce the bioaccumulation potential of chemicals. The intensified physical activity enhances the overall elimination kinetics of chemicals from the human body. However, the simulated concentrations of chemicals in the human body increase with higher physical activity levels, due to the significantly increased external exposure to chemicals, such as through inhalation. Our proposed modeling approach, along with the operational tool, enables high-throughput simulation of human chronic internal exposure to chemicals at different physical activity levels, where the findings can assist in screening chemicals for further health risk assessment. To accomplish this, the model incorporates certain assumptions and utilizes generic model input values. However, due to the intricate nature of the interaction between external and internal exposures at different physical activity levels, validating the simulation through experimental studies becomes challenging and is not performed in this study. For future studies, we recommend incorporating more MET-related physiological input variables, improving energy balance estimates, comprehending external exposure estimates, and conducting cohort studies to enhance and validate the proposed modeling approach.

Preparation procedures of food and beverage samples for oxygen bomb calorimetry: A scoping review and reporting checklist

Standardised bomb calorimetry methods are essential to accurately quantify the gross energy within food and beverages, yet no accepted protocols exist. The objective of this review was to synthesise literature on food and beverage sample preparation methods used for conducting bomb calorimetry. This synthesis enhances our understanding of the extent to which methodological variances may currently affect estimates of the caloric values of dietary items. Five electronic databases were searched for peer reviewed literature on food and beverage energy measurement via bomb calorimetry. Data were extracted on seven identified methodological themes, including: (1) initial homogenisation, (2) sample dehydration, (3) post-dehydration homogenisation, (4) sample presentation, (5) sample weight, (6) sample frequency, and (7) equipment calibration. A tabular and narrative approach was used to synthesise the data. Studies that specifically explored the impact of any methodological variance on the energy derived from foods and/or beverages were also considered. In total, 71 documents describing food and beverage sample preparation techniques and processes used for bomb calorimetry were identified. Only 8% of studies described all seven identified sample preparation and calibration processes. The most frequent approaches used included: initial homogenisation - mixing or blending (n = 21); sample dehydration - freeze drying (n = 37); post-dehydration homogenisation - grinding (n = 24); sample presentation - pelletisation (n = 29); sample weight - 1g (n = 14); sample frequency - duplicate (n = 17); and equipment calibration - benzoic acid (n = 30). The majority of studies that have measured food and beverage energy via bomb calorimetry do not describe sample preparation and calibration methods in detail. The extent to which different sample preparation processes influence the energy derived from food and beverage items is yet to be fully elucidated. Use of a bomb calorimetry reporting checklist (described within) may assist with improving the methodological quality of bomb calorimetry studies.

Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial

The gut microbiome is emerging as a key modulator of human energy balance. Prior studies in humans lacked the environmental and dietary controls and precision required to quantitatively evaluate the contributions of the gut microbiome. Using a Microbiome Enhancer Diet (MBD) designed to deliver more dietary substrates to the colon and therefore modulate the gut microbiome, we quantified microbial and host contributions to human energy balance in a controlled feeding study with a randomized crossover design in young, healthy, weight stable males and females (NCT02939703). In a metabolic ward where the environment was strictly controlled, we measured energy intake, energy expenditure, and energy output (fecal and urinary). The primary endpoint was the within-participant difference in host metabolizable energy between experimental conditions [Control, Western Diet (WD) vs. MBD]. The secondary endpoints were enteroendocrine hormones, hunger/satiety, and food intake. Here we show that, compared to the WD, the MBD leads to an additional 116 ± 56 kcals (P < 0.0001) lost in feces daily and thus, lower metabolizable energy for the host (89.5 ± 0.73%; range 84.2-96.1% on the MBD vs. 95.4 ± 0.21%; range 94.1-97.0% on the WD; P < 0.0001) without changes in energy expenditure, hunger/satiety or food intake (P > 0.05). Microbial 16S rRNA gene copy number (a surrogate of biomass) increases (P < 0.0001), beta-diversity changes (whole genome shotgun sequencing; P = 0.02), and fermentation products increase (P < 0.01) on an MBD as compared to a WD along with significant changes in the host enteroendocrine system (P < 0.0001). The substantial interindividual variability in metabolizable energy on the MBD is explained in part by fecal SCFAs and biomass. Our results reveal the complex host-diet-microbiome interplay that modulates energy balance.

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.

Developing a model for estimating the activity of colonic microbes after intestinal surgeries

Background

The large intestine provides a compensatory role in energy recovery when surgical interventions such as extensive small intestinal resections or bypass operations lower the efficiency of nutrient absorption in the upper gastrointestinal (GI) tract. While microorganisms in the colon are known to play vital roles in recovering energy, their contributions remain to be qualified and quantified in the small intestine resection.

Objective

We develop a mathematical model that links nutrient absorption in the upper and lower GI tract in two steps.

Methods

First, we describe the effects of small intestine resection on the ileocecal output (ICO), which enters the colon and provides food for microbes. Second, we describe energy recovered by the colon’s microorganisms via short-chain fatty acid (SCFA) production. We obtain model parameters by performing a least-squares regression analysis on clinical data for subjects with normal physiology and those who had undergone small intestine resection.

Results

For subjects with their intestines intact, our model provided a metabolizable energy value that aligns well with the traditional Atwater coefficients. With removal of the small intestine, physiological absorption became less efficient, and the metabolizable energy decreased. In parallel, the inefficiencies in physiological absorption by the small intestine are partly compensated by production of short-chain fatty acids (SCFA) from proteins and carbohydrates by microorganisms in the colon. The colon recovered more than half of the gross energy intake when the entire small intestine was removed. Meanwhile, the quality of energy absorbed changed, because microbe-derived SCFAs, not the original components of food, become the dominant form of absorbed energy.

Conclusion

The mathematical model developed here provides an important framework for describing the effect of clinical interventions on the colon’s microorganisms.