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

BACKGROUND: Human and microbial metabolism are distinct disciplines. Terminology, metrics, and methodologies have been developed separately. Therefore, combining the 2 fields to study energetic processes simultaneously is difficult. OBJECTIVES: When developing a mechanistic framework describing gut microbiome and human metabolism interactions, energy values of food and digestive materials that use consistent and compatible metrics are required. As an initial step toward this goal, we developed and validated a model to convert between chemical oxygen demand (COD) and gross energy (${E_g}$) for >100 food items and ingredients. METHODS: We developed linear regression models to relate (and be able to convert between) theoretical gross energy (${E_g}^{\prime}$) and chemical oxygen demand (COD'); the latter is a measure of electron equivalents in the food's carbon. We developed an overall regression model for the food items as a whole and separate regression models for the carbohydrate, protein, and fat components. The models were validated using a sample set of computed ${E_g}^{\prime}$ and COD' values, an experimental sample set using measured ${E_g}$ and COD values, and robust statistical methods. RESULTS: The overall linear regression model and the carbohydrate, protein, and fat regression models accurately converted between COD and ${E_g}$, and the component models had smaller error. Because the ratios of COD per gram dry weight were greatest for fats and smallest for carbohydrates, foods with a high fat content also had higher ${E_g}$ values in terms of kcal · g dry weight-1. CONCLUSION: Our models make it possible to analyze human and microbial energetic processes in concert using a single unit of measure, which fills an important need in the food-nutrition-metabolism-microbiome field. In addition, measuring COD and using the regressions to calculate ${E_g}$ can be used instead of measuring ${E_g}$ directly using bomb calorimetry, which saves time and money.

Impact of vessel wall lesions and vascular stenoses on cerebrovascular reactivity in patients with intracranial stenotic disease.

PURPOSE: To compare cerebrovascular reactivity (CVR) and CVR lagtimes in flow territories perfused by vessels with vs. without proximal arterial wall disease and/or stenosis, separately in patients with atherosclerotic and nonatherosclerotic (moyamoya) intracranial stenosis. MATERIALS AND METHODS: Atherosclerotic and moyamoya patients with >50% intracranial stenosis and <70% cervical stenosis underwent angiography, vessel wall imaging (VWI), and CVR-weighted imaging (n = 36; vessel segments evaluated = 396). Angiography and VWI were evaluated for stenosis locations and vessel wall lesions. Maximum CVR and CVR lagtime were contrasted between vascular territories with and without proximal intracranial vessel wall lesions and stenosis, and a Wilcoxon rank-sum was test used to determine differences (criteria: corrected two-sided P < 0.05). RESULTS: CVR lagtime was prolonged in territories with vs. without a proximal vessel wall lesion or stenosis for both patient groups: moyamoya (CVR lagtime = 45.5 sec ± 14.2 sec vs. 35.7 sec ± 9.7 sec, P < 0.001) and atherosclerosis (CVR lagtime = 38.2 sec ± 9.1 sec vs. 35.0 sec ± 7.2 sec, P = 0.001). For reactivity, a significant decrease in maximum CVR in the moyamoya group only (maximum CVR = 9.8 ± 2.2 vs. 12.0 ± 2.4, P < 0.001) was observed. CONCLUSION: Arterial vessel wall lesions detected on noninvasive, noncontrast intracranial VWI in patients with intracranial stenosis correlate on average with tissue-level impairment on CVR-weighted imaging. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2017;46:1167-1176.

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.

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.

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.

Learning Implicit Brain MRI Manifolds with Deep Learning.

An important task in image processing and neuroimaging is to extract quantitative information from the acquired images in order to make observations about the presence of disease or markers of development in populations. Having a low-dimensional manifold of an image allows for easier statistical comparisons between groups and the synthesis of group representatives. Previous studies have sought to identify the best mapping of brain MRI to a low-dimensional manifold, but have been limited by assumptions of explicit similarity measures. In this work, we use deep learning techniques to investigate implicit manifolds of normal brains and generate new, high-quality images. We explore implicit manifolds by addressing the problems of image synthesis and image denoising as important tools in manifold learning. First, we propose the unsupervised synthesis of T1-weighted brain MRI using a Generative Adversarial Network (GAN) by learning from 528 examples of 2D axial slices of brain MRI. Synthesized images were first shown to be unique by performing a cross-correlation with the training set. Real and synthesized images were then assessed in a blinded manner by two imaging experts providing an image quality score of 1-5. The quality score of the synthetic image showed substantial overlap with that of the real images. Moreover, we use an autoencoder with skip connections for image denoising, showing that the proposed method results in higher PSNR than FSL SUSAN after denoising. This work shows the power of artificial networks to synthesize realistic imaging data, which can be used to improve image processing techniques and provide a quantitative framework to structural changes in the brain.