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Modelling approaches ranging from \u201cbottom-up\u201d mechanistic models to \u201ctop-down\u201d 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\u2019 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 <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> 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.<\/jats:p>","DOI":"10.1371\/journal.pone.0285820","type":"journal-article","created":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T17:40:38Z","timestamp":1690479638000},"page":"e0285820","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":5,"title":["Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8643-4915","authenticated-orcid":true,"given":"Bal\u00e1zs","family":"Erd\u0151s","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2827-9889","authenticated-orcid":true,"given":"Bart","family":"van Sloun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gijs H.","family":"Goossens","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shauna D.","family":"O\u2019Donovan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bastiaan E.","family":"de Galan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marleen M. 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Playing with numbers: the development of an educational diabetes game. 2017;."},{"issue":"3","key":"pone.0285820.ref009","doi-asserted-by":"crossref","first-page":"e1008852","DOI":"10.1371\/journal.pcbi.1008852","article-title":"Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge","volume":"17","author":"B Erd\u0151s","year":"2021","journal-title":"PLoS computational biology"},{"issue":"6","key":"pone.0285820.ref010","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1016\/j.cell.2021.02.012","article-title":"Metabolic consequences of obesity and type 2 diabetes: Balancing genes and environment for personalized care","volume":"184","author":"NJ Pillon","year":"2021","journal-title":"Cell"},{"key":"pone.0285820.ref011","first-page":"1","article-title":"Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions","author":"N Tsereteli","year":"2021","journal-title":"Diabetologia"},{"key":"pone.0285820.ref012","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.artmed.2019.07.007","article-title":"Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes","volume":"98","author":"AZ Woldaregay","year":"2019","journal-title":"Artificial intelligence in medicine"},{"key":"pone.0285820.ref013","doi-asserted-by":"crossref","first-page":"173","DOI":"10.3389\/fpubh.2020.00173","article-title":"Application of artificial intelligence in diabetes education and management: present status and promising prospect","volume":"8","author":"J Li","year":"2020","journal-title":"Frontiers in public health"},{"issue":"6","key":"pone.0285820.ref014","doi-asserted-by":"crossref","first-page":"e0253125","DOI":"10.1371\/journal.pone.0253125","article-title":"Machine learning-based glucose prediction with use of continuous glucose and physical activity monitoring data: The Maastricht Study","volume":"16","author":"WP van Doorn","year":"2021","journal-title":"PloS one"},{"issue":"5","key":"pone.0285820.ref015","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1016\/j.cell.2015.11.001","article-title":"Personalized nutrition by prediction of glycemic responses","volume":"163","author":"D Zeevi","year":"2015","journal-title":"Cell"},{"issue":"12","key":"pone.0285820.ref016","first-page":"1","article-title":"Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling","volume":"16","author":"D Lee","year":"2020","journal-title":"PLOS Computational Biology"},{"issue":"8","key":"pone.0285820.ref017","article-title":"Hybrid Modelling by Machine Learning Corrections of Analytical Model Predictions towards High-Fidelity Simulation Solutions","volume":"14","author":"FE Bock","year":"2021","journal-title":"Materials"},{"key":"pone.0285820.ref018","article-title":"Metabolic Modeling Combined With Machine Learning Integrates Longitudinal Data and Identifies the Origin of LXR-Induced Hepatic Steatosis","volume":"8","author":"NAW van Riel","year":"2021","journal-title":"Frontiers in Bioengineering and Biotechnology"},{"issue":"6","key":"pone.0285820.ref019","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s10654-014-9889-0","article-title":"The Maastricht Study: an extensive phenotyping study on determinants of type 2 diabetes, its complications and its comorbidities","volume":"29","author":"MT Schram","year":"2014","journal-title":"European Journal of Epidemiology"},{"issue":"Supplement 1","key":"pone.0285820.ref020","doi-asserted-by":"crossref","first-page":"S8","DOI":"10.2337\/dc15-S005","article-title":"2. Classification and diagnosis of diabetes","volume":"38","author":"AD Association","year":"2015","journal-title":"Diabetes care"},{"issue":"2","key":"pone.0285820.ref021","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.mbs.2013.03.006","article-title":"Parameter uncertainty in biochemical models described by ordinary differential equations","volume":"246","author":"J Vanlier","year":"2013","journal-title":"Mathematical biosciences"},{"key":"pone.0285820.ref022","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD\u201916. New York, NY, USA: ACM; 2016. p. 785\u2013794. 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