TY - JOUR
T1 - A data-driven computational model for obesity-driven diabetes onset and remission through weight loss
AU - Yildirim, Vehpi
AU - Sheraton, Vivek M.
AU - Brands, Ruud
AU - Crielaard, Loes
AU - Quax, Rick
AU - van Riel, Natal A. W.
AU - Stronks, Karien
AU - Nicolaou, Mary
AU - Sloot, Peter M. A.
N1 - Funding Information: This work was supported by a grant by ZonMw with the project number 531003015 . R.Q. also acknowledges the EU Horizon 2020 project TO_AITION ( nr 848146 ). V.Y. received partial support through a personal grant by TUBITAK ( nr 121F278 ). We also thank Albert K. Groen and Richard Bertram for their insightful remarks and helpful suggestions on the manuscript. Publisher Copyright: © 2023 The Author(s)
PY - 2023/11/17
Y1 - 2023/11/17
N2 - 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.
AB - 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.
KW - Bioinformatics
KW - Computational bioinformatics
KW - Human metabolism
UR - http://www.scopus.com/inward/record.url?scp=85176419070&partnerID=8YFLogxK
UR - https://pure.uva.nl/ws/files/152544794/Supplemental_Information-A_data-driven_computational_model_for_obesity-driven_diabetes.pdf
U2 - https://doi.org/10.1016/j.isci.2023.108324
DO - https://doi.org/10.1016/j.isci.2023.108324
M3 - Article
C2 - 38026205
SN - 2589-0042
VL - 26
JO - iScience
JF - iScience
IS - 11
M1 - 108324
ER -