Model-based analysis of postprandial glycemic response dynamics for different types of food

Does GLP-1 cause post-bariatric hypoglycemia: 'Computer says no'

BACKGROUND AND OBJECTIVE

Patients who underwent Roux-en-Y Gastric Bypass surgery for treatment of obesity or diabetes can suffer from post-bariatric hypoglycemia (PBH). It has been assumed that PBH is caused by increased levels of the hormone GLP-1. In this research, we elucidate the role of GLP-1 in PBH with a physiology-based mathematical model.

METHODS

The Eindhoven Diabetes Simulator (EDES) model, simulating postprandial glucose homeostasis, was adapted to include the effect of GLP-1 on insulin secretion. Parameter sensitivity analysis was used to identify parameters that could cause PBH. Virtual patient models were created by defining sets of models parameters based on 63 participants from the HypoBaria study cohort, before and one year after bariatric surgery.

RESULTS

Simulations with the virtual patient models showed that glycemic excursions can be correctly simulated for the study population, despite heterogeneity in the glucose, insulin and GLP-1 data. Sensitivity analysis showed that GLP-1 stimulated insulin secretion alone was not able to cause PBH. Instead, analyses showed the increased transit speed of the ingested food resulted in quick and increased glucose absorption in the gut after surgery, which in turn induced postprandial glycemic dips. Furthermore, according to the model post-bariatric increased rate of glucose absorption in combination with different levels of insulin sensitivity can result in PBH.

CONCLUSIONS

Our model findings implicate that if initial rapid improvement in insulin sensitivity after gastric bypass surgery is followed by a more gradual decrease in insulin sensitivity, this may result in the emergence of PBH after prolonged time (months to years after surgery).

Blood Glucose Prediction from Nutrition Analytics in Type 1 Diabetes: A Review

INTRODUCTION

Type 1 Diabetes (T1D) affects over 9 million worldwide and necessitates meticulous self-management for blood glucose (BG) control. Utilizing BG prediction technology allows for increased BG control and a reduction in the diabetes burden caused by self-management requirements. This paper reviews BG prediction models in T1D, which include nutritional components.

METHOD

A systematic search, utilizing the PRISMA guidelines, identified articles focusing on BG prediction algorithms for T1D that incorporate nutritional variables. Eligible studies were screened and analyzed for model type, inclusion of additional aspects in the model, prediction horizon, patient population, inputs, and accuracy.

RESULTS

The study categorizes 138 blood glucose prediction models into data-driven (54%), physiological (14%), and hybrid (33%) types. Prediction horizons of ≤30 min are used in 36% of models, 31-60 min in 34%, 61-90 min in 11%, 91-120 min in 10%, and >120 min in 9%. Neural networks are the most used data-driven technique (47%), and simple carbohydrate intake is commonly included in models (data-driven: 72%, physiological: 52%, hybrid: 67%). Real or free-living data are predominantly used (83%).

CONCLUSION

The primary goal of blood glucose prediction in T1D is to enable informed decisions and maintain safe BG levels, considering the impact of all nutrients for meal planning and clinical relevance.

The power of berries against cardiovascular diseases

Cardiovascular diseases (CVDs) pose a serious threat to human health and incidence is increasing gradually. Nutrition has an important impact on the prophylaxis and progression of CVD. In this article, general attention is drawn to the possible positive effects of berries on CVD. Polyphenols have beneficial effects on the vascular system by inhibiting low-density lipoprotein oxidation and platelet aggregation, lowering blood pressure, improving endothelial dysfunction, and attenuating antioxidant defense and inflammatory responses. This review provides an overview of the effects of berries for the prevention and treatment of CVDs. Berries contain several cardioprotective antioxidants, vitamins, and numerous phytochemicals, such as phenolic compounds, that have antioxidant properties and antiplatelet activity. Phytochemical compounds in their structures can modulate dissimilar signaling pathways related to cell survival, differentiation, and growth. Important health benefits of berries include their antioxidant roles and anti-inflammatory impacts on vascular function. The effectiveness and potential of polyphenols primarily depend on the amount of bioavailability and intake. Although circulating berry metabolites can improve vascular function, their biological activities, mechanisms of action, and in vivo interactions are still unknown. Analyzing human studies or experimental studies to evaluate the bioactivity of metabolites individually and together is essential to understanding the mechanisms by which these metabolites affect vascular function.

E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans

Current computational models of whole-body glucose homeostasis describe physiological processes by which insulin regulates circulating glucose concentrations. While these models perform well in response to oral glucose challenges, interaction with other nutrients that impact postprandial glucose metabolism, such as amino acids (AAs), is not considered. Here, we developed a computational model of the human glucose-insulin system, which incorporates the effects of AAs on insulin secretion and hepatic glucose production. This model was applied to postprandial glucose and insulin time-series data following different AA challenges (with and without co-ingestion of glucose), dried milk protein ingredients, and dairy products. Our findings demonstrate that this model allows accurate description of postprandial glucose and insulin dynamics and provides insight into the physiological processes underlying meal responses. This model may facilitate the development of computational models that describe glucose homeostasis following the intake of multiple macronutrients, while capturing relevant features of an individual's metabolic health.

Glycemic Responses of Milk and Plant-Based Drinks: Food Matrix Effects

The consumption of food items containing digestible carbohydrates in food products leads to postprandial increases in blood glucose levels and glycemic responses. The extent to which these occur depends on many factors, including concentration and type of carbohydrate, but also other physicochemical properties of the food matrix, which determine the rate of uptake of monosaccharides into the bloodstream, including product structure and factors affecting gastric emptying. For milk, control of postprandial glycemic responses appears to be multifaceted, including a controlled rate of gastric emptying, a rate of glucose and galactose uptake into the bloodstream controlled by enzymatic hydrolysis, as well as stimulated insulin secretion to enhance uptake of blood glucose from the bloodstream. Altogether, this allows milk to deliver comparatively high levels of carbohydrate with limited glycemic responses. For plant-based drinks positioned as milk alternatives, however, compositional differences (including carbohydrate type and concentration) as well as matrix factors limiting control over gastric emptying and insulin secretion can, in some cases, lead to much stronger glycemic responses, which are undesirable in relation to non-communicable diseases, such as type-2 diabetes. This review discusses glycemic responses to milk and plant-based drinks from this perspective, focusing on mechanistic insights and food matrix effects.

Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model

Despite the pivotal role played by elevated circulating triglyceride levels in the pathophysiology of cardio-metabolic diseases many of the indices used to quantify metabolic health focus on deviations in glucose and insulin alone. We present the Mixed Meal Model, a computational model describing the systemic interplay between triglycerides, free fatty acids, glucose, and insulin. We show that the Mixed Meal Model can capture deviations in the post-meal excursions of plasma glucose, insulin, and triglyceride that are indicative of features of metabolic resilience; quantifying insulin resistance and liver fat; validated by comparison to gold-standard measures. We also demonstrate that the Mixed Meal Model is generalizable, applying it to meals with diverse macro-nutrient compositions. In this way, by coupling triglycerides to the glucose-insulin system the Mixed Meal Model provides a more holistic assessment of metabolic resilience from meal response data, quantifying pre-clinical metabolic deteriorations that drive disease development in overweight and obesity.

Reduced Carbohydrate Diet Influence on Postprandial Glycemia—Results of a Short, CGM-Based, Interventional Study in Adolescents with Type 1 Diabetes

Therapy for type 1 diabetes (T1DM) focuses on maintaining optimal blood glucose levels, achieved with intensive insulin treatment, proper nutrition, and physical activity. The aim of this study was to investigate postprandial glycemic changes under low (30%) and standard (50%) carbohydrate diets in adolescents with T1DM. A single-center cross-over nutritional study was conducted, during which 26 adolescent patients provided 220 continuous glucose-monitored (CGM) meals data from the two consecutive 3-day nutritional plans. Overall, the 50% carbohydrate diet was associated with higher postprandial glucose variability in the small meals (afternoon snacks, second breakfast) and greater postprandial peaks for other meals (breakfast, dinner, supper). Nevertheless, after the adjustment of a patient’s individual clinical variables (age, Tanner classification, glucose disposal rate), we observed that mean postprandial glucose was higher for afternoon snacks and lower for suppers in the 30% carbohydrate diet. Although a 30% carbohydrate diet seems to offer better postprandial glycemia, it requires additional attention from the physician and patient when it comes to modifying daily carbohydrate intake. Increased fat/protein content and size of the main meal lead to a prolonged postprandial glycemic response, which may affect the insulin treatment and result in suboptimal glycemic control.

Glycaemia dynamics in gestational diabetes mellitus

Pregnant women may develop gestational diabetes mellitus (GDM), a disease of pregnancy characterised by maternal and fetal hyperglycaemia with hazardous consequences to the mother, the fetus, and the newborn. Maternal hyperglycaemia in GDM results in fetoplacental endothelial dysfunction. GDM-harmful effects result from chronic and short periods of hyperglycaemia. Thus, it is determinant to keep glycaemia within physiological ranges avoiding short but repetitive periods of hyper or hypoglycaemia. The variation of glycaemia over time is defined as 'glycaemia dynamics'. The latter concept regards with a variety of mechanisms and environmental conditions leading to blood glucose handling. In this review we summarized the different metrics for glycaemia dynamics derived from quantitative, plane distribution, amplitude, score values, variability estimation, and time series analysis. The potential application of the derived metrics from self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) in the potential alterations of pregnancy outcome in GDM are discussed.

Uncontrolled eating in healthy women has limited influence on food cue reactivity and food-related inhibitory control

Uncontrolled eating-in the general population-is characterized by overeating, hedonic hunger and being drawn towards palatable foods. Theoretically, it is the result of a strong food reward signal in relation to a poor ability to exert inhibitory control. How food consumption influences inhibitory control and food cue sensitivity, and how this relates to the continued urge to eat, remains unclear. We used fMRI in order to investigate the neural mechanism underlying food cue reactivity and food-specific response inhibition (go-nogo task), by comparing women reporting high (n = 21) versus low/average (n = 19) uncontrolled eating across two sessions: during an inter-meal state and after consumption of a high-caloric snack. We found no effects of individual differences in uncontrolled eating, food consumption, nor their interaction on food cue reactivity. Differences in uncontrolled eating and food consumption did interact in modulating activity in an occipital-parietal network, extending from left lateral superior occipital cortex to visual cortex, cuneal cortex, and precuneus during response inhibition of non-food stimuli, areas previously associated with successful nogo-vs. go-trials. Yet, behavioural performance on the go-nogo task was not modulated by uncontrolled eating nor food consumption. Women with a low/average tendency for uncontrolled eating may need more cognitive resources to support successful response inhibition of non-food stimuli during food 'go' blocks in an inter-meal state, whereas women with a high tendency for uncontrolled eating showed this after food consumption. However, considering current and previous findings, it seems that individual differences in uncontrolled eating in healthy women have only limited influence on food cue reactivity and food-related inhibitory control.

Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis

Temporal multi-omics data can provide information about the dynamics of disease development and therapeutic response. However, statistical analysis of high-dimensional time-series data is challenging. Here we develop a novel approach to model temporal metabolomic and transcriptomic data by combining machine learning with metabolic models. ADAPT (Analysis of Dynamic Adaptations in Parameter Trajectories) performs metabolic trajectory modeling by introducing time-dependent parameters in differential equation models of metabolic systems. ADAPT translates structural uncertainty in the model, such as missing information about regulation, into a parameter estimation problem that is solved by iterative learning. We have now extended ADAPT to include both metabolic and transcriptomic time-series data by introducing a regularization function in the learning algorithm. The ADAPT learning algorithm was (re)formulated as a multi-objective optimization problem in which the estimation of trajectories of metabolic parameters is constrained by the metabolite data and refined by gene expression data. ADAPT was applied to a model of hepatic lipid and plasma lipoprotein metabolism to predict metabolic adaptations that are induced upon pharmacological treatment of mice by a Liver X receptor (LXR) agonist. We investigated the excessive accumulation of triglycerides (TG) in the liver resulting in the development of hepatic steatosis. ADAPT predicted that hepatic TG accumulation after LXR activation originates for 80% from an increased influx of free fatty acids. The model also correctly estimated that TG was stored in the cytosol rather than transferred to nascent very-low density lipoproteins. Through model-based integration of temporal metabolic and gene expression data we discovered that increased free fatty acid influx instead of de novo lipogenesis is the main driver of LXR-induced hepatic steatosis. This study illustrates how ADAPT provides estimates for biomedically important parameters that cannot be measured directly, explaining (side-)effects of pharmacological treatment with LXR agonists.

Determining the glycaemic responses of foods: conventional and emerging approaches

A low-glycaemic diet is crucial for those with diabetes and cardiovascular diseases. Information on the glycaemic index (GI) of different ingredients can help in designing novel food products for such target groups. This is because of the intricate dependency of material source, composition, food structure and processing conditions, among other factors, on the glycaemic responses. Different approaches have been used to predict the GI of foods, and certain discrepancies exist because of factors such as inter-individual variation among human subjects. Besides other aspects, it is important to understand the mechanism of food digestion because an approach to predict GI must essentially mimic the complex processes in the human gastrointestinal tract. The focus of this work is to review the advances in various approaches for predicting the glycaemic responses to foods. This has been carried out by detailing conventional approaches, their merits and limitations, and the need to focus on emerging approaches. Given that no single approach can be generalised to all applications, the review emphasises the scope of deriving insights for improvements in methodologies. Reviewing the conventional and emerging approaches for the determination of GI in foods, this detailed work is intended to serve as a state-of-the-art resource for nutritionists who work on developing low-GI foods.

Model-based data analysis of individual human postprandial plasma bile acid responses indicates a major role for the gallbladder and intestine

BACKGROUND

Bile acids are multifaceted metabolic compounds that signal to cholesterol, glucose, and lipid homeostasis via receptors like the Farnesoid X Receptor (FXR) and transmembrane Takeda G protein-coupled receptor 5 (TGR5). The postprandial increase in plasma bile acid concentrations is therefore a potential metabolic signal. However, this postprandial response has a high interindividual variability. Such variability may affect bile acid receptor activation.

METHODS

In this study, we analyzed the inter- and intraindividual variability of fasting and postprandial bile acid concentrations during three identical meals on separate days in eight healthy lean male subjects using a statistical and mathematical approach.

MAIN FINDINGS

The postprandial bile acid responses exhibited large interindividual and intraindividual variability. The individual mathematical models, which represent the enterohepatic circulation of bile acids in each subject, suggest that interindividual variability results from quantitative and qualitative differences of distal active uptake, colon transit, and microbial bile acid transformation. Conversely, intraindividual variations in gallbladder kinetics can explain intraindividual differences in the postprandial responses.

CONCLUSIONS

We conclude that there is considerable inter- and intraindividual variation in postprandial plasma bile acid levels. The presented personalized approach is a promising tool to identify unique characteristics of underlying physiological processes and can be applied to investigate bile acid metabolism in pathophysiological conditions.

Physiologically Based Modeling of Food Digestion and Intestinal Microbiota: State of the Art and Future Challenges. An INFOGEST Review

This review focuses on modeling methodologies of the gastrointestinal tract during digestion that have adopted a systems-view approach and, more particularly, on physiologically based compartmental models of food digestion and host-diet-microbiota interactions. This type of modeling appears very promising for integrating the complex stream of mechanisms that must be considered and retrieving a full picture of the digestion process from mouth to colon. We may expect these approaches to become more and more accurate in the future and to serve as a useful means of understanding the physicochemical processes occurring in the gastrointestinaltract, interpreting postprandial in vivo data, making relevant predictions, and designing healthier foods. This review intends to provide a scientific and historical background of this field of research, before discussing the future challenges and potential benefits of the establishment of such a model to study and predict food digestion and absorption in humans.

Blueberries and cardiovascular disease prevention

Blueberries are a rich source of (poly)phenols, particularly anthocyanins. Epidemiological studies indicate that anthocyanin-rich foods including blueberries are associated with a reduction in the risk of cardiovascular disease. These observational findings are supported by a number of randomized-controlled trials showing improvements in biomarkers of cardiovascular disease risk. The beneficial effects of blueberry (poly)phenols are particularly clear when measuring flow-mediated dilation over various timeframes and study populations. However, other outcomes are less clear, such as effects on blood pressure, arterial stiffness and blood lipid profile. This may be due to the heterogeneity existing in study designs, such as duration of the intervention, and the health status of participants. Longer-term RCTs using gold standard methods in relevant populations which can be translated to the general public are needed to clarify and strengthen the evidence available. While circulating phenolic blueberry metabolites have been linked with improvements in vascular function, the biological activities and mechanisms of action of individual metabolites and their interaction in vivo are still unknown. Evaluating the bioactivities of metabolites alone and together, and analysing their structure-activity relationship in well-designed and physiologically relevant experimental and human studies are needed to understand the mechanisms of how these metabolites affect vascular function.

A computational model of postprandial adipose tissue lipid metabolism derived using human arteriovenous stable isotope tracer data

Given the association of disturbances in non-esterified fatty acid (NEFA) metabolism with the development of Type 2 Diabetes and Non-Alcoholic Fatty Liver Disease, computational models of glucose-insulin dynamics have been extended to account for the interplay with NEFA. In this study, we use arteriovenous measurement across the subcutaneous adipose tissue during a mixed meal challenge test to evaluate the performance and underlying assumptions of three existing models of adipose tissue metabolism and construct a new, refined model of adipose tissue metabolism. Our model introduces new terms, explicitly accounting for the conversion of glucose to glyceraldehye-3-phosphate, the postprandial influx of glycerol into the adipose tissue, and several physiologically relevant delays in insulin signalling in order to better describe the measured adipose tissues fluxes. We then applied our refined model to human adipose tissue flux data collected before and after a diet intervention as part of the Yoyo study, to quantify the effects of caloric restriction on postprandial adipose tissue metabolism. Significant increases were observed in the model parameters describing the rate of uptake and release of both glycerol and NEFA. Additionally, decreases in the model's delay in insulin signalling parameters indicates there is an improvement in adipose tissue insulin sensitivity following caloric restriction.

A Minimal Model Approach for the Description of Postprandial Glucose Responses from Glucose Sensor Data in Diabetes Mellitus

Modelling of the gluco-regulatory system in response to an oral glucose tolerance test (OGTT) has been the subject of research for decades. This paper presents an adaptation to the well-established oral minimal model that is identifiable from glucose data only and is able to capture the dynamics of glucose following both OGTT and mixed meal consumption. The model is in the form of low-dimensional differential equations with a recently introduced input function consisting of Gaussian shaped components. It was identified from glucose data recorded from six subjects without diabetes, prediabetes and type 2 diabetes under controlled conditions. The inferred parameters of the model are shown to have physiological meaning and produce realistic steady state behavior. This model may be useful in the development of clinical advisory tools for the treatment and prevention of non-insulin dependent type 2 diabetes mellitus.

Insulin in Type 1 and Type 2 Diabetes-Should the Dose of Insulin Before a Meal be Based on Glycemia or Meal Content?

The aim of this review was to investigate existing guidelines and scientific evidence on determining insulin dosage in people with type 1 and type 2 diabetes, and in particular to check whether the prandial insulin dose should be calculated based on glycemia or the meal composition, including the carbohydrates, protein and fat content in a meal. By exploring the effect of the meal composition on postprandial glycemia we demonstrated that several factors may influence the increase in glycemia after the meal, which creates significant practical difficulties in determining the appropriate prandial insulin dose. Then we reviewed effects of the existing insulin therapy regimens on glycemic control. We demonstrated that in most existing algorithms aimed at calculating prandial insulin doses in type 1 diabetes only carbohydrates are counted, whereas in type 2 diabetes the meal content is often not taken into consideration. We conclude that prandial insulin doses in treatment of people with diabetes should take into account the pre-meal glycemia as well as the size and composition of meals. However, there are still open questions regarding the optimal way to adjust a prandial insulin dose to a meal and the possible benefits for people with type 1 and type 2 diabetes if particular parameters of the meal are taken into account while calculating the prandial insulin dose. The answers to these questions may vary depending on the type of diabetes.