Natal van Riel
Research profile
Natal van Riel is Professor of Biomedical Systems Biology at the department of Biomedical Engineering (research group Computational Biology) at Eindhoven University of Technology, where he leads the Systems Biology and Metabolic Diseases research program. He is also part-time Professor of Computational Modelling at Amsterdam University Medical Centers (location AMC, University of Amsterdam's Faculty of Medicine). His research focuses on modelling of metabolic networks and physiology, machine learning for parameter estimation, methods for analysis of dynamic models, and applications in Metabolic Syndrome and associated diseases such as Type 2 Diabetes. <br/>The role of bile acids in the complex interaction between gut microbiome and metabolic health is currently an important research focus in his group (e.g. in the RESOLVE project in collaboration with Amsterdam UMC). Within the NWO program ‘Complexity in Health and Nutrition’, Natal van Riel also focuses on modelling the digestion and metabolism of nutrients in the project ‘Metabolic adaptation, transitions and resilience in indivuduals suffering overweight. In cooperation with the Catharina hospital in Eindhoven he has developed the Metabolic Health Index (MHI) to quantify the benefit of bariatric surgery to resolve metabolic diseases (type 2 diabetes, dyslipidemia), which is a second important outcome of the surgical treatment in addition to weight reduction. He develops metabolic 'digital twins' of human individuals to enable predictive, preventive, personalized, and participatory medicine. In the DiaGame project digital twins are developed to empower patients with diabetes in self-management of their disease.
Research description
As health conditions improve worldwide, the population lives longer. Longer lives need to go hand in hand with healthy ageing and quality of life. However, in populations of 55 years old and above, specially those with poor socioeconomical status and education, the risk of suffering diseases like obesity, hyperlipidemias, and type II diabetes increases. Obesity and hyperlipidemias can be treated conservativity with nutrition, exercise and weight management. When the weight problem is more serious, surgical options like bariatric surgery are available to bring the patient back to healthy conditions. Additionally, hypoglycemic agents, exercise and nutrition regimes are available for type II diabetic patients. Nonetheless, in all of these treatment options, the crosstalk between food intake and tissue/organs like the liver, skeletal muscle, brains, kidney, gastrointestinal system, and pancreas is unknown. Currently, endocrinologists and internal medicine physicians have few treatment plans for obese and/or diabetic patients. A one-size fits all approach seems to be the norm, and there is no individual knowledge of the patient’s response to food intake and the postprandial carbohydrate, fat, and protein levels at the blood and tissue level. Currently, there is a need of detailed and individualized physiological models of human metabolism that could improve treatment in obese and diabetic patients.
In this challenge, we aim to generate a DT strategy to predict postprandial glucose spikes with the help of continous glucose monitoring wearables and metabolism models, to recomend optimal nutrition and prevent type-II diabetes.
Imagine you start with a heath evaluation of a patient (a healthy or obese individual). The treating physician wants to know more about the patient’s metabolic profile and recommends sensors and wearables (constant glucose monitoring devices or smart watches) to monitor the patient’s response to a meal, heart rate, and oxygen saturation. The data collected by wearables and sensors is fed into a digital twin model that integrates models to predict postprandial glucose response. The model outcome can assist dieticians, clinicians and trainers in developing the most optimal nutrition and treatment plan to control weight, promote physical activity, and improve overall health.
Selected publications
de Carvalho, D.F., Kaymak, U., Van Gorp, P. and van Riel, N., 2022. A Markov model for inferring event types on diabetes patients data. Healthcare Analytics, 2, p.100024.
Wortelboer, K., Bakker, G.J., Winkelmeijer, M., van Riel, N., Levin, E., Nieuwdorp, M., Herrema, H. and Davids, M., 2022. Fecal microbiota transplantation as tool to study the interrelation between microbiota composition and miRNA expression. Microbiological Research, 257, p.126972.
van der Stam, J.A., Mestrom, E.H., Scheerhoorn, J., Jacobs, F., de Hingh, I.H., van Riel, N.A., Boer, A.K., Scharnhorst, V., Nienhuijs, S.W. and Bouwman, R.A., 2022. Accuracy of vital parameters measured by a wearable patch following major abdominal cancer surgery. European Journal of Surgical Oncology, 48(4), pp.917-923.