A data-driven computational model for obesity-driven diabetes onset and remission through weight loss

Vehpi Yildirim; Vivek M Sheraton; Ruud Brands; Loes Crielaard; Rick Quax; Natal A W van Riel; Karien Stronks; Mary Nicolaou; Peter M A Sloot

doi:10.1016/j.isci.2023.108324

A data-driven computational model for obesity-driven diabetes onset and remission through weight loss

iScience. 2023 Oct 23;26(11):108324. doi: 10.1016/j.isci.2023.108324. eCollection 2023 Nov 17.

Authors

Vehpi Yildirim^{1

2}, Vivek M Sheraton^{2

3

4}, Ruud Brands^{5

6}, Loes Crielaard^{1

2}, Rick Quax^{2

3}, Natal A W van Riel^{7

8}, Karien Stronks^{1

2}, Mary Nicolaou^{1

2}, Peter M A Sloot^{2

3}

Affiliations

¹ Department of Public and Occupational Health, Amsterdam University Medical Centers, University of Amsterdam, 1081 BT Amsterdam, the Netherlands.
² Institute for Advanced Study, University of Amsterdam, 1012 GC Amsterdam, the Netherlands.
³ Computational Science Lab, University of Amsterdam, 1098 XH Amsterdam, the Netherlands.
⁴ Center for Experimental and Molecular Medicine, Amsterdam University Medical Centers, 1100 DD Amsterdam, the Netherlands.
⁵ AMRIF B.V., Agro Business Park, 6708 PW Wageningen, the Netherlands.
⁶ Institute for Risk Assessment Sciences, Utrecht University, 3584 CL Utrecht, the Netherlands.
⁷ Department of Biomedical Engineering, Eindhoven University of Technology, 5600 MB Eindhoven, the Netherlands.
⁸ Department of Experimental and Vascular Medicine, Amsterdam University Medical Centers, 1100 DD Amsterdam, the Netherlands.

Abstract

Obesity is a major risk factor for the development of type 2 diabetes (T2D), where a sustained weight loss may result in T2D remission in individuals with obesity. To design effective and feasible intervention strategies to prevent or reverse T2D, it is imperative to study the progression of T2D and remission together. Unfortunately, this is not possible through experimental and observational studies. To address this issue, we introduce a data-driven computational model and use human data to investigate the progression of T2D with obesity and remission through weight loss on the same timeline. We identify thresholds for the emergence of T2D and necessary conditions for remission. We explain why remission is only possible within a window of opportunity and the way that window depends on the progression history of T2D, individual's metabolic state, and calorie restrictions. These findings can help to optimize therapeutic intervention strategies for T2D prevention or treatment.

Keywords: Bioinformatics; Computational bioinformatics; Human metabolism.