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

Vehpi Yildirim (Corresponding author)
, Vivek M. Sheraton
, Ruud Brands
, Loes Crielaard
, Rick Quax
, Natal A.W. van Riel
, Karien Stronks
, Mary Nicolaou
, Peter M.A. Sloot

Research output: Contribution to journal › Article › Academic › peer-review

11 Citations (Scopus)

142 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 language	English
Article number	108324
Number of pages	25
Journal	iScience
Volume	26
Issue number	11
DOIs	https://doi.org/10.1016/j.isci.2023.108324
Publication status	Published - 17 Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 The Author(s)

Funding

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.

Funders	Funder number
ZonMw : Dutch Organisation for Health Research and Development	531003015
European Union's Horizon 2020 - Research and Innovation Framework Programme	848146

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Bioinformatics
Computational bioinformatics
Human metabolism

Access to Document

10.1016/j.isci.2023.108324

pdfFinal published version, 8.43 MBLicence: CC BY

Cite this

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title = "A data-driven computational model for obesity-driven diabetes onset and remission through weight loss",

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",

author = "Vehpi Yildirim and Sheraton, \{Vivek M.\} and Ruud Brands and Loes Crielaard and Rick Quax and \{van Riel\}, \{Natal A.W.\} and Karien Stronks and Mary Nicolaou and Sloot, \{Peter M.A.\}",

note = "Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = nov,

day = "17",

doi = "10.1016/j.isci.2023.108324",

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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.

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

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SN - 2589-0042

VL - 26

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JF - iScience

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ER -

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

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

MATRyOShka: Metabolic adaptation, transitions and resilience in overweight individuals

RESOLVE: A systems biology approach to RESOLVE the molecular pathology of two hallmarks of patients with metabolic syndrome and its co-morbidities; hypertriglyceridemia and low HDL-cholesterol

Cite this

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

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Projects

MATRyOShka: Metabolic adaptation, transitions and resilience in overweight individuals

RESOLVE: A systems biology approach to RESOLVE the molecular pathology of two hallmarks of patients with metabolic syndrome and its co-morbidities; hypertriglyceridemia and low HDL-cholesterol

Cite this