Predicting Changes of Body Weight, Body Fat, Energy Expenditure and Metabolic Fuel Selection in C57BL/6 Mice

The mouse is an important model organism for investigating the molecular mechanisms of body weight regulation, but a quantitative understanding of mouse energy metabolism remains lacking. Therefore, we created a mathematical model of mouse energy metabolism to predict dynamic changes of body weight, body fat, energy expenditure, and metabolic fuel selection. Based on the principle of energy balance, we constructed ordinary differential equations representing the dynamics of body fat mass (FM) and fat-free mass (FFM) as a function of dietary intake and energy expenditure (EE). The EE model included the cost of tissue deposition, physical activity, diet-induced thermogenesis, and the influence of FM and FFM on metabolic rate. The model was calibrated using previously published data and validated by comparing its predictions to measurements in five groups of male C57/BL6 mice (N = 30) provided ad libitum access to either chow or high fat diets for varying time periods. The mathematical model accurately predicted the observed body weight and FM changes. Physical activity was predicted to decrease immediately upon switching from the chow to the high fat diet and the model coefficients relating EE to FM and FFM agreed with previous independent estimates. Metabolic fuel selection was predicted to depend on a complex interplay between diet composition, the degree of energy imbalance, and body composition. This is the first validated mathematical model of mouse energy metabolism and it provides a quantitative framework for investigating energy balance relationships in mouse models of obesity and diabetes

A STUDY OF GLUCOSE METABOLISM AND KETOSIS DEVELOPMENT IN PERIPARTURIENT COWS USING A MECHANISTIC MODEL

Periparturient cows are susceptible to ketosis. An animal trial was conducted to evaluate the effect of a transition diet on production performance and ketone body (KB) accumulation. The transition diet was fed from 14 days before expected parturition to 14 days after calving. The energy density and nonstructural carbohydrate content in the transition diet was lower compared to the lactation diet, but higher compared to the non-lactating cow diet. Production performance was not affected by transition diet. Plasma glycerol may be an important contributor to gluconeogenesis during the periparturient period. Feeding a transition diet around parturition was associated with greater mobilization of adipose tissue and greater exposure to KB in early lactation. Data from the animal trial were used to develop a mechanistic model to quantify the interrelationship between glucose and lipid metabolism in periparturient cows. The driving variables of the model were dry matter intake, feed composition, calf birth weight, milk production, and milk components. The response variables were body fat content and concentrations of plasma glucose, glycerol, nonesterified fatty acids and total KB. Comparison of model predictions to data collected in an independent experiment revealed that the model over-predicted glucose and KB concentrations by 0.62 and 0.37 mM, respectively. Calf birth weight, dry matter intake, milk yield, and body condition score were increased by one standard deviation to estimate the model response in KB formation. The responses to the increases in the model parameters (e.g. the rate of fat mobilization) were evaluated to identify the likely critical control points in the animal. The model demonstrated that utilization rate of nonesterified fatty acids has a greater impact on KB concentrations in the first few days of lactation than the other parameters tested in the model. Glucose deficiency was closely related to the rate of fat mobilization. And, the excessive KB could result from elevated fat mobilization for glycerol to compensate for the negative glucose balance in periparturient cows. It is important to avoid overfeeding during the pre-lactation period to prevent ketosis development

The Progressive Increase of Food Waste in America and Its Environmental Impact

Food waste contributes to excess consumption of freshwater and fossil fuels which, along with methane and CO2 emissions from decomposing food, impacts global climate change. Here, we calculate the energy content of nationwide food waste from the difference between the US food supply and the food consumed by the population. The latter was estimated using a validated mathematical model of metabolism relating body weight to the amount of food eaten. We found that US per capita food waste has progressively increased by ∼50% since 1974 reaching more than 1400 kcal per person per day or 150 trillion kcal per year. Food waste now accounts for more than one quarter of the total freshwater consumption and ∼300 million barrels of oil per year

Estimating the Continuous-Time Dynamics of Energy and Fat Metabolism in Mice

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 applied our methodology to male C57BL/6 mice consuming various ad libitum diets during weight gain and loss over several weeks and present the first continuous-time estimates of energy output and net fat oxidation rates underlying the observed body composition changes. We show that transient energy and fat imbalances in the first several days following a diet switch can account for a significant fraction of the total body weight change. We also discovered a time-invariant curve relating body fat and fat-free masses in male C57BL/6 mice, and the shape of this curve determines how diet, fuel selection, and body composition are interrelated

Calorie for calorie, dietary fat restriction results in more body fat loss than carbohydrate restriction in people with obesity

© 2015 Elsevier Inc. Dietary carbohydrate restriction has been purported to cause endocrine adaptations that promote body fat loss more than dietary fat restriction. We selectively restricted dietary carbohydrate versus fat for 6 days following a 5-day baseline diet in 19 adults with obesity confined to a metabolic ward where they exercised daily. Subjects received both isocaloric diets in random order during each of two inpatient stays. Body fat loss was calculated as the difference between daily fat intake and net fat oxidation measured while residing in a metabolic chamber. Whereas carbohydrate restriction led to sustained increases in fat oxidation and loss of 53 ± 6 g/day of body fat, fat oxidation was unchanged by fat restriction, leading to 89 ± 6 g/day of fat loss, and was significantly greater than carbohydrate restriction (p = 0.002). Mathematical model simulations agreed with these data, but predicted that the body acts to minimize body fat differences with prolonged isocaloric diets varying in carbohydrate and fat

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Pharmaceutical pollution of the world's rivers

Environmental exposure to active pharmaceutical ingredients (APIs) can have negative effects on the health of ecosystems and humans. While numerous studies have monitored APIs in rivers, these employ different analytical methods, measure different APIs, and have ignored many of the countries of the world. This makes it difficult to quantify the scale of the problem from a global perspective. Furthermore, comparison of the existing data, generated for different studies/regions/continents, is challenging due to the vast differences between the analytical methodologies employed. Here, we present a global-scale study of API pollution in 258 of the world's rivers, representing the environmental influence of 471.4 million people across 137 geographic regions. Samples were obtained from 1,052 locations in 104 countries (representing all continents and 36 countries not previously studied for API contamination) and analyzed for 61 APIs. Highest cumulative API concentrations were observed in sub-Saharan Africa, south Asia, and South America. The most contaminated sites were in low- to middle-income countries and were associated with areas with poor wastewater and waste management infrastructure and pharmaceutical manufacturing. The most frequently detected APIs were carbamazepine, metformin, and caffeine (a compound also arising from lifestyle use), which were detected at over half of the sites monitored. Concentrations of at least one API at 25.7% of the sampling sites were greater than concentrations considered safe for aquatic organisms, or which are of concern in terms of selection for antimicrobial resistance. Therefore, pharmaceutical pollution poses a global threat to environmental and human health, as well as to delivery of the United Nations Sustainable Development Goals

Heritability and genetic correlations explained by common SNPs for metabolic syndrome traits.

We used a bivariate (multivariate) linear mixed-effects model to estimate the narrow-sense heritability (h(2)) and heritability explained by the common SNPs (h(g)(2)) for several metabolic syndrome (MetS) traits and the genetic correlation between pairs of traits for the Atherosclerosis Risk in Communities (ARIC) genome-wide association study (GWAS) population. MetS traits included body-mass index (BMI), waist-to-hip ratio (WHR), systolic blood pressure (SBP), fasting glucose (GLU), fasting insulin (INS), fasting trigylcerides (TG), and fasting high-density lipoprotein (HDL). We found the percentage of h(2) accounted for by common SNPs to be 58% of h(2) for height, 41% for BMI, 46% for WHR, 30% for GLU, 39% for INS, 34% for TG, 25% for HDL, and 80% for SBP. We confirmed prior reports for height and BMI using the ARIC population and independently in the Framingham Heart Study (FHS) population. We demonstrated that the multivariate model supported large genetic correlations between BMI and WHR and between TG and HDL. We also showed that the genetic correlations between the MetS traits are directly proportional to the phenotypic correlations

C57BL/6 mouse data and model predictions for fat mass.

<p>Panel A: measured body weight in male C57BL/6 mice from 5 diet groups: the C group on chow diet (closed black circles), the HF group on high fat diet (closed blue triangles), the EN group on high fat diet plus Ensure (closed pink squares), the HF-C group on high fat diet for 7 wk followed by a switch to chow (open red triangles), and the EN-C group on high fat diet plus Ensure for 7 wk followed by a switch to chow (open green squares). The solid lines labeled are fitted curves to body weight data. Panel B: Measured body fat mass (F) and adjusted fat free mass (FFM) from the 5 groups designated using the same markers as in Panel A. These data were fit to a single exponential function for all groups: (thick line with thin lines representing 95% confidence intervals). Panel C: measured fat mass from the 5 groups where the data point markers was the same as in Panel A. The solid lines are model predictions for fat mass. Panel D: measured fat mass from a separate experiment with two groups of male C57BL/6 mice: group one on chow diet for 33 wk (closed black circles); group two on high fat diet for 20 wk followed by a switch to chow (closed blue triangles). The error bars represent 95% confidence interval for the measurements. The curves represent the model predictions for fat mass in groups one (black) and two (blue).</p