Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

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).

Model selection reveals selective regulation of blood amino acid and lipid metabolism by insulin in humans

Insulin plays a crucial role in regulating the metabolism of blood glucose, amino acids (aa), and lipids in humans. However, the mechanisms by which insulin selectively regulates these metabolites are not fully understood. To address this question, we used mathematical modeling to identify the selective regulatory mechanisms of insulin on blood aa and lipids. Our study revealed that insulin negatively regulates the influx and positively regulates the efflux of lipids, consistent with previous findings. By contrast, we did not observe the previously reported insulin's negative regulation of branched-chain aa (BCAA) influx; instead, we found that insulin positively regulates BCAA efflux. We observed that the earlier peak time of lipids compared to BCAA is dependent on insulin's negative regulation of their influx. Overall, our findings shed new light on how insulin selectively regulates the levels of different metabolites in human blood, providing insights into the metabolic disorder pathogenesis and potential therapies.

Association Between the Postprandial Response to an Oral Glucose Tolerance Test and Anthropometric Changes After an 8-Week Low-Calorie Formula Diet - Results From the Lifestyle Intervention (LION) Study

SCOPE

Interindividual variations in postprandial metabolism and weight loss outcomes have been reported. The literature suggests links between postprandial metabolism and weight regulation. Therefore, the study aims to evaluate if postprandial glucose metabolism after a glucose load predicts anthropometric outcomes of a weight loss intervention.

METHODS AND RESULTS

Anthropometric data from adults with obesity (18-65 years, body mass index [BMI] 30.0-39.9 kg m-2) are collected pre- and post an 8-week formula-based weight loss intervention. An oral glucose tolerance test (OGTT) is performed at baseline, from which postprandial parameters are derived from glucose and insulin concentrations. Linear regression models explored associations between these parameters and anthropometric changes (∆) postintervention. A random forest model is applied to identify predictive parameters for anthropometric outcomes after intervention. Postprandial parameters after an OGTT of 158 participants (63.3% women, age 45 ± 12, BMI 34.9 ± 2.9 kg m-2) reveal nonsignificant associations with changes in anthropometric parameters after weight loss (p > 0.05). Baseline fat-free mass (FFM) and sex are primary predictors for ∆ FFM [kg].

CONCLUSION

Postprandial glucose metabolism after a glucose load does not predict anthropometric outcomes after short-term weight loss via a formula-based low-calorie diet in adults with obesity.

Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health in silico. A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.67, p < 0.05) and the gold-standard hyperinsulinemic-euglycemic clamp. The model is also shown to quantify liver fat accumulation and β-cell functionality. Moreover, we show that personalized Mixed Meal Models can be used to evaluate the impact of a dietary intervention on multiple aspects of metabolic health at the individual level.

Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism

Continuous glucose monitoring (CGM) is a promising, minimally invasive alternative to plasma glucose measurements for calibrating physiology-based mathematical models of insulin-regulated glucose metabolism, reducing the reliance on in-clinic measurements. However, the use of CGM glucose, particularly in combination with insulin measurements, to develop personalized models of glucose regulation remains unexplored. Here, we simultaneously measured interstitial glucose concentrations using CGM as well as plasma glucose and insulin concentrations during an oral glucose tolerance test (OGTT) in individuals with overweight or obesity to calibrate personalized models of glucose-insulin dynamics. We compared the use of interstitial glucose with plasma glucose in model calibration, and evaluated the effects on model fit, identifiability, and model parameters' association with clinically relevant metabolic indicators. Models calibrated on both plasma and interstitial glucose resulted in good model fit, and the parameter estimates associated with metabolic indicators such as insulin sensitivity measures in both cases. Moreover, practical identifiability of model parameters was improved in models estimated on CGM glucose compared to plasma glucose. Together these results suggest that CGM glucose may be considered as a minimally invasive alternative to plasma glucose measurements in model calibration to quantify the dynamics of glucose regulation.

Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study

Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from "bottom-up" mechanistic models to "top-down" data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven modeling approach to quantify individuals' glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.

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.

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.

'Obesities': Position statement on a complex disease entity with multifaceted drivers

Academic medicine fosters research that moves from discovery to translation, at the same time as promoting education of the next generation of professionals. In the field of obesity, the supposed integration of knowledge, discovery and translation research to clinical care is being particularly hampered. The classification of obesity based on the body mass index does not account for several subtypes of obesity. The lack of a universally shared definition of "obesities" makes it impossible to establish the real burden of the different obesity phenotypes. The individual's genotype, adipotype, enterotype and microbiota interplays with macronutrient intake, appetite, metabolism and thermogenesis. Further investigations based on the concept of differently diagnosed "obesities" are required.

Oral glucose tolerance test: Ηow to maximize its diagnostic value in children and adolescents

BACKGROUND

Recently, the validity of the oral glucose tolerance test (OGTT) as a gold-standard test for the diagnosis of glucose dysregulation (GD) has been questioned due to the pre-analytical, analytical, and post-analytical variables which can potentially affect its reproducibility and accuracy.

AIMS

In this short update, the many  variables that affect the reproducibility and accuracy of the OGTT are described and discussed aiming to enhance its diagnostic value  in  clinical practice.

SEARCH STRATEGY

A systematic search was implemented in June 2022, using Scopus, PubMed, Embase and Google Scholar focusing on OGTT relevant  papers published in the last 10 years. Moreover,  the reference lists of these  articles were checked for additional pertinent studies. The research and selection of articles was also supported by the long-term authors' experience in the use of OGTT for the diagnosis of GD in children and adolescents.

CONCLUSION

The complexity of diagnosing GD presupposes that clinicians have specific knowledge and experience to perform rigorous assessment of glucose metabolism. It is worth mentioning that during OGTT, subjects with glucose levels  close to the cut-off values proposed  by WHO (World Health Organization)/ADA (American Diabetes Association) require careful evaluation in order to avoid misclassification and unnecessary interventions. For this reason, ADA recommends a second test to confirm the diagnosis of diabetes.

The PERSonalized Glucose Optimization Through Nutritional Intervention (PERSON) Study: Rationale, Design and Preliminary Screening Results

Background: It is well-established that the etiology of type 2 diabetes differs between individuals. Insulin resistance (IR) may develop in different tissues, but the severity of IR may differ in key metabolic organs such as the liver and skeletal muscle. Recent evidence suggests that these distinct tissue-specific IR phenotypes may also respond differentially to dietary macronutrient composition with respect to improvements in glucose metabolism.

Objective: The main objective of the PERSON study is to investigate the effects of an optimal vs. suboptimal dietary macronutrient intervention according to tissue-specific IR phenotype on glucose metabolism and other health outcomes.

Methods: In total, 240 overweight/obese (BMI 25 – 40 kg/m²) men and women (age 40 – 75 years) with either skeletal muscle insulin resistance (MIR) or liver insulin resistance (LIR) will participate in a two-center, randomized, double-blind, parallel, 12-week dietary intervention study. At screening, participants undergo a 7-point oral glucose tolerance test (OGTT) to determine the hepatic insulin resistance index (HIRI) and muscle insulin sensitivity index (MISI), classifying each participant as either “No MIR/LIR,” “MIR,” “LIR,” or “combined MIR/LIR.” Individuals with MIR or LIR are randomized to follow one of two isocaloric diets varying in macronutrient content and quality, that is hypothesized to be either an optimal or suboptimal diet, depending on their tissue-specific IR phenotype (MIR/LIR). Extensive measurements in a controlled laboratory setting as well as phenotyping in daily life are performed before and after the intervention. The primary study outcome is the difference in change in disposition index, which is the product of insulin sensitivity and first-phase insulin secretion, between participants who received their hypothesized optimal or suboptimal diet.

Discussion: The PERSON study is one of the first randomized clinical trials in the field of precision nutrition to test effects of a more personalized dietary intervention based on IR phenotype. The results of the PERSON study will contribute knowledge on the effectiveness of targeted nutritional strategies to the emerging field of precision nutrition, and improve our understanding of the complex pathophysiology of whole body and tissue-specific IR.

Clinical Trial Registration:https://clinicaltrials.gov/ct2/show/NCT03708419, clinicaltrials.gov as NCT03708419.