Relating tissue/organ energy expenditure to metabolic fluxes in mouse and human: experimental data integrated with mathematical modeling (original) (raw)

Measuring energy metabolism in the mouse – theoretical, practical, and analytical considerations

The mouse is one of the most important model organisms for understanding human genetic function and disease. This includes characterization of the factors that influence energy expenditure and dysregulation of energy balance leading to obesity and its sequelae. Measuring energy metabolism in the mouse presents a challenge because the animals are small, and in this respect it presents similar challenges to measuring energy demands in many other species of small mammal.This paper considers some theoretical, practical, and analytical considerations to be considered when measuring energy expenditure in mice. Theoretically total daily energy expenditure is comprised of several different components: basal or resting expenditure, physical activity, thermoregulation, and the thermic effect of food. Energy expenditure in mice is normally measured using open flow indirect calorimetry apparatus. Two types of system are available -one of which involves a single small Spartan chamber linked to a single analyzer, which is ideal for measuring the individual components of energy demand. The other type of system involves a large chamber which mimics the home cage environment and is generally configured with several chambers/analyzer. These latter systems are ideal for measuring total daily energy expenditure but at present do not allow accurate decomposition of the total expenditure into its components. The greatest analytical challenge for mouse expenditure data is how to account for body size differences between individuals. This has been a matter of some discussion for at least 120 years. The statistically most appropriate approach is to use analysis of covariance with individual aspects of body composition as independent predictors.

A guide to analysis of mouse energy metabolism

Nature Methods, 2012

We present a consolidated view of the complexity and challenges of designing studies for measurement of energy metabolism in mouse models, including a practical guide to the assessment of energy expenditure, energy intake and body composition and statistical analysis thereof. We hope this guide will facilitate comparisons across studies and minimize spurious interpretations of data. We recommend that division of energy expenditure data by either body weight or lean body weight and that presentation of group effects as histograms should be replaced by plotting individual data and analyzing both group and body-composition effects using analysis of covariance (ANCOVA).

A Strategy for Development of Realistic Mathematical Models of Whole-Body Metabolism

Open Journal of Applied Sciences, 2012

When realistic mathematical models of whole body metabolism eventually become available, they are likely to add entirely new dimensions to the understanding of the integrated physiological function of the organism, in particular the mechanisms governing the regulation of transitions between different physiological states, like fed-fasted, exercise-rest and normal-diseased. So far the strategy for whole body modelling has primarily been a bottom-up approach where the central problem is an apparently insurmountable barrier of complexity involved in defining and optimising the huge number of parameters. Here we follow a top-down strategy and present a complete mathematical framework for realistic whole body model development. The approach proposed is modular and hierarchical and whole body metabolism is taken as the top level. Next are the organs, where the sum of the contributions from the individual organs must equal the top level metabolism. This hierarchy can be extended to lower levels of organisation, i.e. clusters of cells, individual cells, organelle and individual pathways. Exploiting this hierarchy, metabolism at each level forms an absolute constraint on the contributions from lower level. Importantly, these constraints can in many ways be defined experimentally through mass balance and flux data. Furthermore, the constrained approach allows the lower level models to be developed independently and subsequently adapted to the whole body model. The paper describes the process of whole body modelling in practical terms, centred on a mathematical framework, devised to allow whole-body models of any complexity to be developed. Furthermore, an example of sub-model incorporation in the whole-body framework is illustrated by adapting an existing erythrocyte model to the whole body constraints. Finally, we illustrate the operation of the system by including two sets of whole-body data from humans, reflecting two different physiological states. J (mL/min) pap muscle J (mL/min) pap fat J (mL/min) pap brain J (mL/min) pap heart J (mL/min) pap kidney J (mL/min) pap RBC J (mL/min) pap gut J (mL/min) pap other J (mL/min) pap lungs J (mL/min) 325+800 1000 250 600 200 800 -800 1025 5000 σ i J is the blood flow through organ i [63], denotes the net rate of uptake of the corresponding organ, and Δ σ i b

Estimating energy expenditure in mice using an energy balance technique

International journal of obesity (2005), 2013

To compare, in mice, the accuracy of estimates of energy expenditure (EE) using an energy balance technique (TEEbal: food energy intake and body composition change) vs indirect calorimetry (TEEIC). In 32 male C57BL/6J mice, EE was estimated using an energy balance (caloric intake minus change in body energy stores) method over a 37-day period. EE was also measured in the same animals by indirect calorimetry. These measures were compared. The two methods were highly correlated (r(2)=0.87: TEEbal=1.07*TEEIC-0.22, P<0.0001). By Bland-Altman analysis, TEEbal estimates were slightly higher (4.6±1.5%; P<0.05) than TEEIC estimates (Bias=0.55 kcal per 24 h). TEEbal can be performed in 'home cages' and provides an accurate integrated long-term measurement of EE while minimizing potentially confounding stress that may accompany the use of indirect calorimetry systems. The technique can also be used to assess long-term energy intake.

Human energy expenditure: advances in organ‐tissue prediction models

Obesity Reviews, 2018

SummaryHumans expend energy at rest (REE), and this major energy exchange component is now usually estimated using statistical equations that include weight and other predictor variables. While these formulas are useful in evaluating an individual's or group's REE, an important gap remains: available statistical models are inadequate for explaining underlying organ‐specific and tissue‐specific mechanisms accounting for resting heat production. The lack of such systems level REE prediction models leaves many research questions unanswered. A potential approach that can fill this gap began with investigators who first showed in animals and later in humans that REE reflects the summated heat production rates of individual organs and tissues. Today, using advanced imaging technologies, REE can be accurately estimated from the measured in vivo mass of 10 organ‐tissue mass components combined with their respective mass‐specific metabolic rates. This review examines the next frontie...

Computational modelling of energy balance in individuals with Metabolic Syndrome

BMC Systems Biology

Background: A positive energy balance is considered to be the primary cause of the development of obesityrelated diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations. Results: We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified. In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment. Conclusion: The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditurefor instance by cold exposure to activate brown adipose tissue (BAT)could resolve MetS symptoms.

Integration of body temperature into the analysis of energy expenditure in the mouse

Molecular Metabolism, 2015

Objectives: We quantified the effect of environmental temperature on mouse energy homeostasis and body temperature. Methods: The effect of environmental temperature (4e33 C) on body temperature, energy expenditure, physical activity, and food intake in various mice (chow diet, high-fat diet, Brs3-/y , lipodystrophic) was measured using continuous monitoring. Results: Body temperature depended most on circadian phase and physical activity, but also on environmental temperature. The amounts of energy expenditure due to basal metabolic rate (calculated via a novel method), thermic effect of food, physical activity, and cold-induced thermogenesis were determined as a function of environmental temperature. The measured resting defended body temperature matched that calculated from the energy expenditure using Fourier's law of heat conduction. Mice defended a higher body temperature during physical activity. The cost of the warmer body temperature during the active phase is 4e16% of total daily energy expenditure. Parameters measured in diet-induced obese and Brs3-/y mice were similar to controls. The high post-mortem heat conductance demonstrates that most insulation in mice is via physiological mechanisms. Conclusions: At 22 C, cold-induced thermogenesis is w120% of basal metabolic rate. The higher body temperature during physical activity is due to a higher set point, not simply increased heat generation during exercise. Most insulation in mice is via physiological mechanisms, with little from fur or fat. Our analysis suggests that the definition of the upper limit of the thermoneutral zone should be reconsidered. Measuring body temperature informs interpretation of energy expenditure data and improves the predictiveness and utility of the mouse to model human energy homeostasis.

Estimating the continuous-time dynamics of energy and fat metabolism in mice

PLoS computational biology, 2009

The mouse has become the most popular organism for investigating molecular mechanisms of body weight regulation. But understanding the physiological context by which a molecule exerts its effect on body weight requires knowledge of energy intake, energy expenditure, and fuel selection. Furthermore, measurements of these variables made at an isolated time point cannot explain why body weight has its present value since body weight is determined by the past history of energy and macronutrient imbalance. While food intake and body weight changes can be frequently measured over several weeks (the relevant time scale for mice), correspondingly frequent measurements of energy expenditure and fuel selection are not currently feasible. To address this issue, we developed a mathematical method based on the law of energy conservation that uses the measured time course of body weight and food intake to estimate the underlying continuous-time dynamics of energy output and net fat oxidation. We ...

DXA: Potential for Creating a Metabolic Map of Organ-Tissue Resting Energy Expenditure Components

Obesity Research, 2002

Objective: This study tested the hypothesis that tissue-organ components can be derived from DXA measurements, and in turn, resting energy expenditure (REE) can be calculated from the summed heat productions of DXA-estimated brain, skeletal muscle mass (SM), adipose tissue, bone, and residual mass (RM). Research Methods and Procedures: Subjects were divided into five groups of adults Ͻ50 years of age. The specific metabolic rate of RM was developed in 13 Group I healthy subjects and a DXA-brain mass prediction formula in 52 Group II subjects. SM, adipose tissue, and bone models were developed based on earlier reports. The composite REE prediction model (REEp) was tested in 154 Group III subjects in whom REEp was compared with measured REE (REEm). Features of the developed model were determined in 94 normal-weight men and women (Group IV) and seven spinal cord injury patients and healthy matched controls (Group V). Results: REEp and REEm in Group III were highly correlated (y ϭ 0.85x ϩ 233; r ϭ 0.82, p Ͻ 0.001), and no bias was detected. Both REEm (mean Ϯ SD, 1579 Ϯ 324 kcal/d) and REEp (1585 Ϯ 316 kcal/d) were also highly correlated (r values ϭ 0.85 to 0.98; p values Ͻ 0.001) and provided similar group values to REE estimated by the Harris-Benedict equations (1597 Ϯ 279 kcal/d) and Wang's composite fat-free mass-based REE equation (1547 Ϯ 248 kcal/d). New insights into the sources and distribution of REE were provided by analysis of the demonstration groups. Discussion: This approach offers a new practical and educational opportunity to examine REE in subject groups using modeling strategies that reveal the magnitude and distribution of fundamental somatic heat-producing units.

Progress and challenges in analyzing rodent energy expenditure

Nature Methods, 2019

Whole-body energy expenditure is the summed metabolic activities of tissues and, to remove the influence of body size, ratios of energy expenditure to body mass are often applied but can generate spurious differences. In 2011, a group of experts proposed adoption of ANCOVA for the analysis of metabolic rate but, seven years later, analyses based on ratios remain the most frequent. We discuss some of the barriers to adopting better analytical procedures.