The effect of increased lipid intake on hormonal responses during aerobic exercise in endurance-trained men

In view of the growing health problem associated with obesity, clarification of the regulation of energy homeostasis is important. Peripheral signals, such as ghrelin and leptin, have been shown to influence energy homeostasis. Nutrients and physical exercise, in turn, influence hormone levels. Data on the hormonal response to physical exercise (standardized negative energy balance) after high-fat (HF) or low-fat (LF) diet with identical carbohydrate intake are currently not available. The aim of the study was to investigate whether a short-term dietary intervention with HF and LF affects ghrelin and leptin levels and their modulators, GH, insulin and cortisol, before and during aerobic exercise. Eleven healthy, endurance-trained male athletes (W(max) 365 +/- 29 W) were investigated twice in a randomized crossover design following two types of diet: 1. LF - 0.5 g fat/kg body weight (BW) per day for 2.5 days; 2. HF - 0.5 g fat/kg BW per day for 1 day followed by 3.5 g fat/kg BW per day for 1.5 days. After a standardized carbohydrate snack in the morning, metabolites and hormones (GH, ghrelin, leptin, insulin and cortisol) were measured before and at regular intervals throughout a 3-h aerobic exercise test on a cycloergometer at 50% of W(max). Diet did not significantly affect GH and cortisol concentrations during exercise but resulted in a significant increase in ghrelin and decrease in leptin concentrations after LF compared with HF diet (area under the curve (AUC) ghrelin LF vs HF: P < 0.03; AUC leptin LF vs HF: P < 0.02, Wilcoxon rank test). These data suggest that acute negative energy balance induced by exercise elicits a hormonal response with opposite changes of ghrelin and leptin. In addition, the hormonal response is modulated by the preceding intake of fat.

[When eating becomes dangerous...]

We report the case of a 59-year-old women with idiopathic insulin auto-immune syndrome, a rare cause of endogenous hyperinsulinemic hypoglycemia. It is characterized by extremely high levels of insulin in the presence of high titers of insulin antibodies despite the absence of previous insulin injections. Early postprandial increase in glucose concentrations due to impaired insulin action resulting from the buffering effect of the antibodies and late postprandial hypoglycemia as a consequence of the dissociation of insulin from the antibodies was observed. A correct diagnosis is important to avoid unnecessary investigations and surgery in these patients who are best treated conservatively - with a good prognosis - by fractionating carbohydrate intake during the day.

A real time simulation model of glucose-insulin metabolism for type 1 diabetes patients

In this paper, a simulation model of glucose-insulin metabolism for Type 1 diabetes patients is presented. The proposed system is based on the combination of Compartmental Models (CMs) and artificial Neural Networks (NNs). This model aims at the development of an accurate system, in order to assist Type 1 diabetes patients to handle their blood glucose profile and recognize dangerous metabolic states. Data from a Type 1 diabetes patient, stored in a database, have been used as input to the hybrid system. The data contain information about measured blood glucose levels, insulin intake, and description of food intake, along with the corresponding time. The data are passed to three separate CMs, which produce estimations about (i) the effect of Short Acting (SA) insulin intake on blood insulin concentration, (ii) the effect of Intermediate Acting (IA) insulin intake on blood insulin concentration, and (iii) the effect of carbohydrate intake on blood glucose absorption from the gut. The outputs of the three CMs are passed to a Recurrent NN (RNN) in order to predict subsequent blood glucose levels. The RNN is trained with the Real Time Recurrent Learning (RTRL) algorithm. The resulted blood glucose predictions are promising for the use of the proposed model for blood glucose level estimation for Type 1 diabetes patients.

Short-term feed intake is regulated by macronutrient oxidation in lactating Holstein cows

In addition to plasma metabolites and hormones participating as humoral signals in the control of feed intake, oxidative metabolic processes in peripheral organs also generate signals to terminate feeding. Although the degree of oxidation over longer periods is relatively constant, recent work suggests that the periprandial pattern of fuel oxidation is involved in regulating feeding behavior in the bovine. However, the association between periprandial oxidative metabolism and feed intake of dairy cows has not yet been studied. Therefore, the aim of this study was to elucidate possible associations existing between single feed intake events and whole-body net fat and net carbohydrate oxidation as well as their relation to plasma metabolite concentrations. To this end, 4 late-lactating cows equipped with jugular catheters were kept in respiratory chambers with continuous and simultaneous recording of gas exchange and feed intake. Animals were fed ad libitum (AL) for 24h and then feed restricted (RE) to 50% of the previous AL intake for a further 24h. Blood samples were collected hourly to analyze β-hydroxybutyrate (BHBA), glucose, nonesterified fatty acids (NEFA), insulin, and acylated ghrelin concentrations. Cross-correlation analysis revealed an offset ranging between 30 and 42 min between the maximum of a feed intake event and the lowest level of postprandial net fat oxidation (FOX(net)) and the maximum level of postprandial net carbohydrate oxidation (COX(net)), respectively. During the AL period, FOX(net) did not increase above -0.2g/min, whereas COX(net) did not decrease below 6g/min before the start of the next feed intake event. A strong inverse cross-correlation was obtained between COX(net) and plasma glucose concentration. Direct cross-correlations were observed between COXnet and insulin, between heat production and BHBA, between insulin and glucose, and between BHBA and ghrelin. We found no cross-correlation between FOX(net) and NEFA. During RE, FOX(net) increased with an exponential slope, exceeded the threshold of -0.2g/min as indicated by increasing plasma NEFA concentrations, and approached a maximum rate of 0.1g/min, whereas COX(net) decayed in an exponential manner, approaching a minimal COX(net) rate of about 2.5 g/min in all cows. Our novel findings suggest that, in late-lactating cows, postprandial increases in metabolic oxidative processes seem to signal suppression of feed intake, whereas preprandially an accelerated FOX(net) rate and a decelerated COX(net) rate initiate feed intake.

Ethanol Metabolites: Their Role in the Assessment of Alcohol Intake

BACKGROUND Alcohol-related disorders are common, expensive in their course, and often underdiagnosed. To facilitate early diagnosis and therapy of alcohol-related disorders and to prevent later complications, questionnaires and biomarkers are useful. METHODS Indirect state markers like gamma-glutamyl-transpeptidase, mean corpuscular volume, and carbohydrate deficient transferrin are influenced by age, gender, various substances, and nonalcohol-related illnesses, and do not cover the entire timeline for alcohol consumption. Ethanol (EtOH) metabolites, such as ethyl glucuronide, ethyl sulfate, phosphatidylethanol, and fatty acid ethyl esters have gained enormous interest in the last decades as they are detectable after EtOH intake. RESULTS For each biomarker, pharmacological characteristics, detection methods in different body tissues, sensitivity/specificity values, cutoff values, time frames of detection, and general limitations are presented. Another focus of the review is the use of the markers in special clinical and forensic samples. CONCLUSIONS Depending on the biological material used for analysis, ethanol metabolites can be applied in different settings such as assessment of alcohol intake, screening, prevention, diagnosis, and therapy of alcohol use disorders.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.