1
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Pasveer YM, Aydin Ö, Groen AK, Meijnikman AS, Nieuwdorp M, Gerdes VEA, van Riel NAW. Does GLP-1 cause post-bariatric hypoglycemia: 'Computer says no'. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 257:108424. [PMID: 39326360 DOI: 10.1016/j.cmpb.2024.108424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 07/18/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024]
Abstract
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).
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Affiliation(s)
- Ysanne M Pasveer
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ömrüm Aydin
- Department of Vascular Medicine, Amsterdam UMC - AMC, Amsterdam, The Netherlands
| | - Albert K Groen
- Department of Vascular Medicine, Amsterdam UMC - AMC, Amsterdam, The Netherlands
| | - Abraham S Meijnikman
- Department of Vascular Medicine, Amsterdam UMC - AMC, Amsterdam, The Netherlands
| | - Max Nieuwdorp
- Department of Vascular Medicine, Amsterdam UMC - AMC, Amsterdam, The Netherlands
| | - Victor E A Gerdes
- Department of Vascular Medicine, Amsterdam UMC - AMC, Amsterdam, The Netherlands; Department of Bariatric Surgery, Spaarne Gasthuis, Hoofddorp, The Netherlands; Department of Internal Medicine, Spaarne Gasthuis, Hoofddorp, The Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands; Department of Vascular Medicine, Amsterdam UMC - AMC, Amsterdam, The Netherlands.
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2
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Matabuena M, Sartini J. Multilevel functional data analysis modeling of human glucose response to meal intake. ARXIV 2024:arXiv:2405.14690v1. [PMID: 38827463 PMCID: PMC11142320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM monitors pushes the limits of existing analytic methods. CGM data often exhibits substantial within-person variability and has a natural multilevel structure. This research is motivated by the analysis of CGM data from individuals without diabetes in the AEGIS study. The dataset includes detailed information on meal timing and nutrition for each individual over different days. The primary focus of this study is to examine CGM glucose responses following patients' meals and explore the time-dependent associations with dietary and patient characteristics. Motivated by this problem, we propose a new analytical framework based on multilevel functional models, including a new functional mixed R-square coefficient. The use of these models illustrates 3 key points: (i) The importance of analyzing glucose responses across the entire functional domain when making diet recommendations; (ii) The differential metabolic responses between normoglycemic and prediabetic patients, particularly with regards to lipid intake; (iii) The importance of including random, person-level effects when modelling this scientific problem.
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Affiliation(s)
- Marcos Matabuena
- Universidad de Santiago de Compostela and Department of Biostatistics, Harvard University, Boston, MA 02115, USA
| | - Joe Sartini
- Department of Biostatistics, Johns Hopkins University, Francisco Gude, Universidad de Santiago de Compostela
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3
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O’Donovan SD, Rundle M, Thomas EL, Bell JD, Frost G, Jacobs DM, Wanders A, de Vries R, Mariman EC, van Baak MA, Sterkman L, Nieuwdorp M, Groen AK, Arts IC, van Riel NA, Afman LA. Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models. iScience 2024; 27:109362. [PMID: 38500825 PMCID: PMC10946327 DOI: 10.1016/j.isci.2024.109362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 10/27/2023] [Accepted: 02/26/2024] [Indexed: 03/20/2024] Open
Abstract
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.
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Affiliation(s)
- Shauna D. O’Donovan
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Milena Rundle
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - E. Louise Thomas
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, the United Kingdom
| | - Jimmy D. Bell
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, the United Kingdom
| | - Gary Frost
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Doris M. Jacobs
- Science & Technology, Unilever Foods Innovation Center, Wageningen, the Netherlands
| | - Anne Wanders
- Science & Technology, Unilever Foods Innovation Center, Wageningen, the Netherlands
| | - Ryan de Vries
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Edwin C.M. Mariman
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Marleen A. van Baak
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Luc Sterkman
- Caelus Pharmaceuticals, Zegveld, the Netherlands
| | - Max Nieuwdorp
- Vascular Medicine, Amsterdam UMC Locatie, AMC, Amsterdam, the Netherlands
| | - Albert K. Groen
- Vascular Medicine, Amsterdam UMC Locatie, AMC, Amsterdam, the Netherlands
| | - Ilja C.W. Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Natal A.W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Lydia A. Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
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4
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Erdős B, O'Donovan SD, Adriaens ME, Gijbels A, Trouwborst I, Jardon KM, Goossens GH, Afman LA, Blaak EE, van Riel NAW, Arts ICW. Leveraging continuous glucose monitoring for personalized modeling of insulin-regulated glucose metabolism. Sci Rep 2024; 14:8037. [PMID: 38580749 PMCID: PMC11371931 DOI: 10.1038/s41598-024-58703-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/01/2024] [Indexed: 04/07/2024] Open
Abstract
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.
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Affiliation(s)
- Balázs Erdős
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.
- Department of Data Science and Knowledge Discovery, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.
| | - Shauna D O'Donovan
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Michiel E Adriaens
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Anouk Gijbels
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Inez Trouwborst
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Kelly M Jardon
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Gijs H Goossens
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Ellen E Blaak
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Ilja C W Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
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5
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F de Carvalho D, Kaymak U, Van Gorp P, van Riel N. Data-driven meal events detection using blood glucose response patterns. BMC Med Inform Decis Mak 2023; 23:282. [PMID: 38066494 PMCID: PMC10709931 DOI: 10.1186/s12911-023-02380-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 11/26/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND In the Diabetes domain, events such as meals and exercises play an important role in the disease management. For that, many studies focus on automatic meal detection, specially as part of the so-called artificial [Formula: see text]-cell systems. Meals are associated to blood glucose (BG) variations, however such variations are not peculiar to meals, it mostly comes as a combination of external factors. Thus, general approaches such as the ones focused on glucose signal rate of change are not enough to detect personalized influence of such factors. By using a data-driven individualized approach for meal detection, our method is able to fit real data, detecting personalized meal responses even when such external factors are implicitly present. METHODS The method is split into model training and selection. In the training phase, we start observing meal responses for each individual, and identifying personalized patterns. Occurrences of such patterns are searched over the BG signal, evaluating the similarity of each pattern to each possible signal subsequence. The most similar occurrences are then selected as possible meal event candidates. For that, we include steps for excluding less relevant neighbors per pattern, and grouping close occurrences in time globally. Each candidate is represented by a set of time and response signal related qualitative variables. These variables are used as input features for different binary classifiers in order to learn to classify a candidate as MEAL or NON-MEAL. In the model selection phase, we compare all trained classifiers to select the one that performs better with the data of each individual. RESULTS The results show that the method is able to detect daily meals, providing a result with a balanced proportion between detected meals and false alarms. The analysis on multiple patients indicate that the approach achieves good outcomes when there is enough reliable training data, as this is reflected on the testing results. CONCLUSIONS The approach aims at personalizing the meal detection task by relying solely on data. The premise is that a model trained with data that contains the implicit influence of external factors is able to recognize the nuances of the individual that generated the data. Besides, the approach can also be used to improve data quality by detecting meals, opening opportunities to possible applications such as detecting and reminding users of missing or wrongly informed meal events.
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Affiliation(s)
- Danilo F de Carvalho
- Jheronimus Academy of Data Science, Eindhoven University of Technology, 's-Hertogenbosch, The Netherlands.
| | - Uzay Kaymak
- Jheronimus Academy of Data Science, Eindhoven University of Technology, 's-Hertogenbosch, The Netherlands
| | - Pieter Van Gorp
- Industrial Engineering & Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Natal van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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6
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Erdős B, van Sloun B, Goossens GH, O'Donovan SD, de Galan BE, van Greevenbroek MMJ, Stehouwer CDA, Schram MT, Blaak EE, Adriaens ME, van Riel NAW, Arts ICW. Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study. PLoS One 2023; 18:e0285820. [PMID: 37498860 PMCID: PMC10374070 DOI: 10.1371/journal.pone.0285820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 05/03/2023] [Indexed: 07/29/2023] Open
Abstract
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.
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Affiliation(s)
- Balázs Erdős
- TiFN, Wageningen, Netherlands
- MaCSBio Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
| | - Bart van Sloun
- TiFN, Wageningen, Netherlands
- MaCSBio Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
| | - Gijs H Goossens
- TiFN, Wageningen, Netherlands
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Shauna D O'Donovan
- Division of Human Nutrition and Health, Wageningen University, Wageningen, Netherlands
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Bastiaan E de Galan
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Marleen M J van Greevenbroek
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Coen D A Stehouwer
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, Netherlands
| | - Miranda T Schram
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, Netherlands
- Department of Internal Medicine, Maastricht University Medical Center, Maastricht, Netherlands
- MHeNs School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
- Heart and Vascular Center, Maastricht University Medical Center, Maastricht, Netherlands
| | - Ellen E Blaak
- TiFN, Wageningen, Netherlands
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, Netherlands
| | - Michiel E Adriaens
- TiFN, Wageningen, Netherlands
- MaCSBio Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Ilja C W Arts
- MaCSBio Maastricht Centre for Systems Biology, Maastricht University, Maastricht, Netherlands
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7
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E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans. iScience 2023; 26:106218. [PMID: 36895641 PMCID: PMC9989689 DOI: 10.1016/j.isci.2023.106218] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 01/27/2023] [Accepted: 02/13/2023] [Indexed: 02/19/2023] Open
Abstract
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.
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8
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Shkembi B, Huppertz T. Glycemic Responses of Milk and Plant-Based Drinks: Food Matrix Effects. Foods 2023; 12:foods12030453. [PMID: 36765982 PMCID: PMC9914410 DOI: 10.3390/foods12030453] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 01/13/2023] [Accepted: 01/16/2023] [Indexed: 01/20/2023] Open
Abstract
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.
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Affiliation(s)
- Blerina Shkembi
- Food Quality & Design Group, Wageningen University & Research, 6708WG Wageningen, The Netherlands
| | - Thom Huppertz
- Food Quality & Design Group, Wageningen University & Research, 6708WG Wageningen, The Netherlands
- FrieslandCampina, 3800LE Amersfoort, The Netherlands
- Correspondence:
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9
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O'Donovan SD, Erdős B, Jacobs DM, Wanders AJ, Thomas EL, Bell JD, Rundle M, Frost G, Arts ICW, Afman LA, van Riel NAW. Quantifying the contribution of triglycerides to metabolic resilience through the mixed meal model. iScience 2022; 25:105206. [PMID: 36281448 DOI: 10.1016/j.isci.2022.105206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/01/2022] [Accepted: 09/22/2022] [Indexed: 11/26/2022] Open
Abstract
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.
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Affiliation(s)
- Shauna D O'Donovan
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands.,Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Balázs Erdős
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Doris M Jacobs
- Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
| | - Anne J Wanders
- Unilever Global Food Innovation Centre, Bronland 14, 6708WH Wageningen, the Netherlands
| | - E Louise Thomas
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Jimmy D Bell
- Research Center for Optimal Health, School of Life Sciences, University of Westminster, London, UK
| | - Milena Rundle
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Gary Frost
- Division of Diabetes, Endocrinology, and Metabolism, Department of Medicine, Imperial College London, London, UK
| | - Ilja C W Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Lydia A Afman
- Division of Human Nutrition and Health, Wageningen University, Wageningen, the Netherlands
| | - Natal A W van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands.,Eindhoven Artifical Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, the Netherlands
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10
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Silfvergren O, Simonsson C, Ekstedt M, Lundberg P, Gennemark P, Cedersund G. Digital twin predicting diet response before and after long-term fasting. PLoS Comput Biol 2022; 18:e1010469. [PMID: 36094958 PMCID: PMC9499255 DOI: 10.1371/journal.pcbi.1010469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2021] [Revised: 09/22/2022] [Accepted: 08/04/2022] [Indexed: 11/30/2022] Open
Abstract
Today, there is great interest in diets proposing new combinations of macronutrient compositions and fasting schedules. Unfortunately, there is little consensus regarding the impact of these different diets, since available studies measure different sets of variables in different populations, thus only providing partial, non-connected insights. We lack an approach for integrating all such partial insights into a useful and interconnected big picture. Herein, we present such an integrating tool. The tool uses a novel mathematical model that describes mechanisms regulating diet response and fasting metabolic fluxes, both for organ-organ crosstalk, and inside the liver. The tool can mechanistically explain and integrate data from several clinical studies, and correctly predict new independent data, including data from a new study. Using this model, we can predict non-measured variables, e.g. hepatic glycogen and gluconeogenesis, in response to fasting and different diets. Furthermore, we exemplify how such metabolic responses can be successfully adapted to a specific individual’s sex, weight, height, as well as to the individual’s historical data on metabolite dynamics. This tool enables an offline digital twin technology. Fasting and diet are central components of prevention against cardiovascular disease. Unfortunately, there is little consensus regarding which diet schemes are optimal. This is partially because different clinical studies contribute with different non-connected pieces of knowledge, which have not been fully integrated into a useful and interconnected big picture. In principle, mathematical models describing meal responses could be used for such an integration. However, today’s models still lack critical mechanisms, such as protein metabolism and a dynamic glycogen regulation. Herein, we present a) a new expanded model structure including these mechanisms; b) a set of parameters which can simultaneously describe a wide array of complementary estimation data, in both healthy and diabetic populations; c) a personalisation-script, which allows these generic parameters to be tuned to an individual/sub-population, using demographics (age, weight, height, diabetes status) and historic metabolic data. We exemplify how this personalisation can be used to predict new independent data, including a new clinical study, where a qualitatively new prediction is validated: that an oral protein tolerance test gives a clear response in plasma glucose, after, but not before, a 48h fasting period. Our combined model, parameters, and fitting script lay the foundation for an offline digital twin.
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Affiliation(s)
- Oscar Silfvergren
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
| | - Christian Simonsson
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
| | - Mattias Ekstedt
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Lundberg
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
- Department of Medical Radiation Physics, and Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Peter Gennemark
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
- Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, IMT, Linköping University, Linköping, Sweden
- Center for Medical Image Science and Visualisation, Linköping University, Linköping, Sweden
- * E-mail:
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Visentin R, Cobelli C, Dalla Man C. A software interface for in silico testing of type 2 diabetes treatments. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 223:106973. [PMID: 35792365 DOI: 10.1016/j.cmpb.2022.106973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 06/09/2022] [Accepted: 06/21/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The increasing incidence of diabetes continuously stimulates the research on new antidiabetic drugs. Computer simulation can save time and costs, alleviating the need of animal trials and providing useful information for optimal experiment design and drug dosing. We recently presented a type 2 diabetes (T2D) simulator as tool for in silico testing of new molecules and guiding treatment optimization. Here we present a user-friendly interface aimed to increase the usability of the simulator. METHOD The simulator, based on a large-scale glucose, insulin, and C-peptide model and equipped with 100 virtual subjects well describing system dynamics in a real T2D population, is extended to incorporate pharmacokinetics/pharmacodynamics (PK/PD) of a drug of interest. A graphical interface is developed on top of the simulator, allowing an easy design of in silico experiments: specifically, it is possible to select the population size to test, design the experiment (crossover or parallel), its duration and the sampling grid, choose glucose and insulin doses, and define treatment PK/PD and dose administered. The simulator also provides the outcome metrics requested by the user, and performs statistical comparisons among treatments and/or placebo. RESULTS To illustrate the potential of the simulator, we provided a case study using metformin and liraglutide. Literature-based PK/PD models of metformin and liraglutide have been incorporated in the simulator, by modulating key drug-sensitive model parameters. An in silico placebo-controlled trial has been done by simulating a three-arm meal tolerance test with subjects receiving placebo, metformin 850 mg, liraglutide 1.80 mg, respectively. The obtained results are in agreement with the clinical evidences, in terms of main glucose, insulin, and C-peptide outcome metrics. CONCLUSIONS We developed a user-friendly software interface for the T2D simulator to support the design and test of new antidiabetic drugs and treatments. This increases the simulator usability, making it suitable also for users who have low experience with computer programming.
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Affiliation(s)
- Roberto Visentin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Claudio Cobelli
- Department of Woman and Child's Health, University of Padova, Padova, Italy
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy.
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Herrgårdh T, Li H, Nyman E, Cedersund G. An Updated Organ-Based Multi-Level Model for Glucose Homeostasis: Organ Distributions, Timing, and Impact of Blood Flow. Front Physiol 2021; 12:619254. [PMID: 34140893 PMCID: PMC8204084 DOI: 10.3389/fphys.2021.619254] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 04/22/2021] [Indexed: 11/13/2022] Open
Abstract
Glucose homeostasis is the tight control of glucose in the blood. This complex control is important, due to its malfunction in serious diseases like diabetes, and not yet sufficiently understood. Due to the involvement of numerous organs and sub-systems, each with their own intra-cellular control, we have developed a multi-level mathematical model, for glucose homeostasis, which integrates a variety of data. Over the last 10 years, this model has been used to insert new insights from the intra-cellular level into the larger whole-body perspective. However, the original cell-organ-body translation has during these years never been updated, despite several critical shortcomings, which also have not been resolved by other modeling efforts. For this reason, we here present an updated multi-level model. This model provides a more accurate sub-division of how much glucose is being taken up by the different organs. Unlike the original model, we now also account for the different dynamics seen in the different organs. The new model also incorporates the central impact of blood flow on insulin-stimulated glucose uptake. Each new improvement is clear upon visual inspection, and they are also supported by statistical tests. The final multi-level model describes >300 data points in >40 time-series and dose-response curves, resulting from a large variety of perturbations, describing both intra-cellular processes, organ fluxes, and whole-body meal responses. We hope that this model will serve as an improved basis for future data integration, useful for research and drug developments within diabetes.
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Affiliation(s)
- Tilda Herrgårdh
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Hao Li
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Elin Nyman
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Gunnar Cedersund
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
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Erdős B, van Sloun B, Adriaens ME, O’Donovan SD, Langin D, Astrup A, Blaak EE, Arts ICW, van Riel NAW. Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge. PLoS Comput Biol 2021; 17:e1008852. [PMID: 33788828 PMCID: PMC8011733 DOI: 10.1371/journal.pcbi.1008852] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 03/03/2021] [Indexed: 01/19/2023] Open
Abstract
Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.
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Affiliation(s)
- Balázs Erdős
- TiFN, Wageningen, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Bart van Sloun
- TiFN, Wageningen, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Michiel E. Adriaens
- TiFN, Wageningen, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Shauna D. O’Donovan
- Division of Human Nutrition and Health, Wageningen University, Wageningen, The Netherlands
| | - Dominique Langin
- Institut National de la Santé et de la Recherche Médicale (INSERM), Université Paul Sabatier Toulouse III, UMR1048, Institute of Metabolic and Cardiovascular Diseases, Laboratoire de Biochimie, CHU Toulouse, Toulouse, France
| | - Arne Astrup
- Department of Nutrition, Exercise and Sports, Faculty of Science, University of Copenhagen, Copenhagen, Denmark
| | - Ellen E. Blaak
- TiFN, Wageningen, The Netherlands
- Department of Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Ilja C. W. Arts
- TiFN, Wageningen, The Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
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14
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Maas AH, van der Molen P, van de Vijver R, Chen W, van Pul C, Cottaar EJ, van Riel NA, Hilbers PA, Haak HR. Concept Development of the Eindhoven Diabetes Education Simulator Project. Games Health J 2016; 5:120-7. [DOI: 10.1089/g4h.2015.0037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Affiliation(s)
- Anne H. Maas
- Department of Internal Medicine, Máxima Medical Center, Eindhoven, The Netherlands
- Design of Technology and Instrumentation, Eindhoven University of Technology, Eindhoven, The Netherlands
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Pieta van der Molen
- Department of Internal Medicine, Máxima Medical Center, Eindhoven, The Netherlands
- User System Interaction, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Reinier van de Vijver
- Department of Internal Medicine, Máxima Medical Center, Eindhoven, The Netherlands
- HBO-ICT, Fontys University for Applied Sciences, Eindhoven, The Netherlands
| | - Wei Chen
- Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands
- Center for Sensor Systems and Intelligent E-health, Department of Electronic Engineering, School of Information Science and Technology, Fudan University, Shanghai, China
| | - Carola van Pul
- Clinical Physics, Máxima Medical Center, Veldhoven, The Netherlands
| | - Eduardus J.E. Cottaar
- Design of Technology and Instrumentation, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Natal A.W. van Riel
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Peter A.J. Hilbers
- Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Harm R. Haak
- Department of Internal Medicine, Máxima Medical Center, Eindhoven, The Netherlands
- Department of Internal Medicine, Division of General Internal Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
- Ageing and Long-Term Care, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands
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