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

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
2 Downloads (Pure)

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.

Original languageEnglish
Article number108324
Pages (from-to)108324
JournaliScience
Volume26
Issue number11
DOIs
Publication statusPublished - 17 Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Fingerprint

Dive into the research topics of 'A data-driven computational model for obesity-driven diabetes onset and remission through weight loss'. Together they form a unique fingerprint.

Cite this