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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] [What about the content of this article? (0)] [Affiliation(s)] [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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O’Donovan SD, Cavill R, Wimmenauer F, Lukas A, Stumm T, Smirnov E, Lenz M, Ertaylan G, Jennen DGJ, van Riel NAW, Driessens K, Peeters RLM, de Kok TMCM. Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data. PLoS One 2023; 18:e0292030. [PMID: 38032940 PMCID: PMC10688741 DOI: 10.1371/journal.pone.0292030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/11/2023] [Indexed: 12/02/2023] Open
Abstract
The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.
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Affiliation(s)
- Shauna D. O’Donovan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Rachel Cavill
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Florian Wimmenauer
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Alexander Lukas
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Tobias Stumm
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Evgueni Smirnov
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
- Preventive Cardiology and Preventative Medicine – Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gokhan Ertaylan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Sustainable Health, Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Danyel G. J. Jennen
- Dept. of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
- Eindhoven Artificial Intelligence Systems Institute (EAISI), Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Kurt Driessens
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Ralf L. M. Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Advanced Computing Sciences, Maastricht University, Maastricht, The Netherlands
| | - Theo M. C. M. de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Toxicogenomics, GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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O’Donovan SD, Driessens K, Lopatta D, Wimmenauer F, Lukas A, Neeven J, Stumm T, Smirnov E, Lenz M, Ertaylan G, Jennen DGJ, van Riel NAW, Cavill R, Peeters RLM, de Kok TMCM. Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes. PLoS One 2020; 15:e0236392. [PMID: 32780735 PMCID: PMC7418976 DOI: 10.1371/journal.pone.0236392] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 07/06/2020] [Indexed: 11/19/2022] Open
Abstract
In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before progressing to human trials. However, relating the results of animal and in vitro model exposures to relevant clinical outcomes in the human in vivo system still proves challenging, relying on often putative orthologs. In recent years, multiple studies have demonstrated that the repeated dose rodent bioassay, the current gold standard in the field, lacks sufficient sensitivity and specificity in predicting toxic effects of pharmaceuticals in humans. In this study, we evaluate the potential of deep learning techniques to translate the pattern of gene expression measured following an exposure in rodents to humans, circumventing the current reliance on orthologs, and also from in vitro to in vivo experimental designs. Of the applied deep learning architectures applied in this study the convolutional neural network (CNN) and a deep artificial neural network with bottleneck architecture significantly outperform classical machine learning techniques in predicting the time series of gene expression in primary human hepatocytes given a measured time series of gene expression from primary rat hepatocytes following exposure in vitro to a previously unseen compound across multiple toxicologically relevant gene sets. With a reduction in average mean absolute error across 76 genes that have been shown to be predictive for identifying carcinogenicity from 0.0172 for a random regression forest to 0.0166 for the CNN model (p < 0.05). These deep learning architecture also perform well when applied to predict time series of in vivo gene expression given measured time series of in vitro gene expression for rats.
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Affiliation(s)
- Shauna D. O’Donovan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Division of Human Nutrition and Health, Wageningen University and Research, Wageningen, The Netherlands
| | - Kurt Driessens
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Daniel Lopatta
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Florian Wimmenauer
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Alexander Lukas
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Jelmer Neeven
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Tobias Stumm
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Evgueni Smirnov
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
- Preventive Cardiology and Preventative Medicine—Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Gokhan Ertaylan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Flemish Institute for Technological Research (VITO), Mol, Belgium
| | - Danyel G. J. Jennen
- Dept. of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Biomedical Engineering, Eindhoven University of Technology, The Netherlands
| | - Rachel Cavill
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Ralf L. M. Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Theo M. C. M. de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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O’Donovan SD, Lenz M, Vink RG, Roumans NJT, de Kok TMCM, Mariman ECM, Peeters RLM, van Riel NAW, van Baak MA, Arts ICW. A computational model of postprandial adipose tissue lipid metabolism derived using human arteriovenous stable isotope tracer data. PLoS Comput Biol 2019; 15:e1007400. [PMID: 31581241 PMCID: PMC6890259 DOI: 10.1371/journal.pcbi.1007400] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 12/03/2019] [Accepted: 09/13/2019] [Indexed: 12/16/2022] Open
Abstract
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.
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Affiliation(s)
- Shauna D. O’Donovan
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Division of Human Health and Nurtrition, Wageningen University, Wageningen, The Netherlands
- * E-mail:
| | - Michael Lenz
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Institute of Organismic and Molecular Evolution, Johannes Gutenberg University Mainz, Mainz, Germany
- Preventive Cardiology and Preventative Medicine - Center for Cardiology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Roel G. Vink
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Nadia J. T. Roumans
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Theo M. C. M. de Kok
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Edwin C. M. Mariman
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Ralf L. M. Peeters
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Data Science and Knowledge Engineering, Maastricht University, Maastricht, The Netherlands
| | - Natal A. W. van Riel
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Marleen A. van Baak
- Dept. Human Biology, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University, Maastricht, The Netherlands
| | - Ilja C. W. Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Dept. Epidemiology, CARIM School for Cardiovascular Disease, Maastricht University, Maastricht, The Netherlands
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