Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients.
Cataract
Diabetes
Diabetic complications
Digital twin
Metabolic flux analysis
Retinopathy
Journal
Health information science and systems
ISSN: 2047-2501
Titre abrégé: Health Inf Sci Syst
Pays: England
ID NLM: 101638060
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
08
03
2022
accepted:
19
02
2023
medline:
4
4
2023
entrez:
3
4
2023
pubmed:
4
4
2023
Statut:
epublish
Résumé
Chronic metabolic diseases arise from changes in metabolic fluxes through biomolecular pathways and gene networks accumulated over the lifetime of an individual. While clinical and biochemical profiles present just real-time snapshots of the patients' health, efficient computation models of the pathological disturbance of biomolecular processes are required to achieve individualized mechanistic insights into disease progression. Here, we describe the Generalized metabolic flux analysis (GMFA) for addressing this gap. Suitably grouping individual metabolites/fluxes into pools simplifies the analysis of the resulting more coarse-grain network. We also map non-metabolic clinical modalities onto the network with additional edges. Instead of using the time coordinate, the system status (metabolite concentrations and fluxes) is quantified as function of a generalized extent variable (a coordinate in the space of generalized metabolites) that represents the system's coordinate along its evolution path and evaluates the degree of change between any two states on that path. We applied GMFA to analyze Type 2 Diabetes Mellitus (T2DM) patients from two cohorts: EVAS (289 patients from Singapore) and NHANES (517) from the USA. Personalized systems biology models (digital twins) were constructed. We deduced disease dynamics from the individually parameterized metabolic network and predicted the evolution path of the metabolic health state. For each patient, we obtained an individual description of disease dynamics and predict an evolution path of the metabolic health state. Our predictive models achieve an ROC-AUC in the range 0.79-0.95 (sensitivity 80-92%, specificity 62-94%) in identifying phenotypes at the baseline and predicting future development of diabetic retinopathy and cataract progression among T2DM patients within 3 years from the baseline. The GMFA method is a step towards realizing the ultimate goal to develop practical predictive computational models for diagnostics based on systems biology. This tool has potential use in chronic disease management in medical practice. The online version contains supplementary material available at 10.1007/s13755-023-00218-x.
Identifiants
pubmed: 37008895
doi: 10.1007/s13755-023-00218-x
pii: 218
pmc: PMC10060506
doi:
Types de publication
Journal Article
Langues
eng
Pagination
18Informations de copyright
© The Author(s) 2023.
Déclaration de conflit d'intérêts
Competing InterestsAB, AW and WL are employed by Mesh Bio, Pte. Ltd. FE is a member of the Advisory Board of Mesh Bio, Pte. Ltd.
Références
J Investig Med. 2020 Aug;68(6):1159-1165
pubmed: 32595133
Occup Environ Med. 1996 Aug;53(8):567-72
pubmed: 8983469
New Horiz Transl Med. 2017 Mar;3(6):294-305
pubmed: 29094062
Exp Hematol. 2018 Feb;58:52-58
pubmed: 28947392
Metabolites. 2019 Jun 06;9(6):
pubmed: 31174372
Biochem Mol Biol Educ. 2020 Sep;48(5):452-459
pubmed: 32604468
Endocr J. 2006 Oct;53(5):665-70
pubmed: 16902260
Clin Biochem. 2014 Apr;47(6):349-55
pubmed: 24373925
Sci Rep. 2017 Jan 30;7:41681
pubmed: 28134328
Hypertension. 1998 Jul;32(1):9-15
pubmed: 9674631
J Diabetes Res. 2020 Jul 31;2020:7219852
pubmed: 32832563
Biomed Res Int. 2019 Jun 9;2019:8304260
pubmed: 31281846
J Theor Biol. 1987 Nov 21;129(2):189-209
pubmed: 3455462
Bioinformatics. 2019 Sep 1;35(17):3063-3072
pubmed: 30649194
J Diabetes Res. 2013;2013:193514
pubmed: 23671858
Clin Lipidol. 2012 Dec 1;7(6):661-675
pubmed: 23646066
Lipids Health Dis. 2019 Apr 1;18(1):82
pubmed: 30935396
Genome Biol. 2019 Jun 13;20(1):121
pubmed: 31196170
Proc Natl Acad Sci U S A. 2000 May 9;97(10):5528-33
pubmed: 10805808
PLoS One. 2018 Jan 11;13(1):e0186582
pubmed: 29324740
Biochem Soc Trans. 1995 May;23(2):341-66
pubmed: 7672373
Clin J Am Soc Nephrol. 2010 Jul;5(7):1277-81
pubmed: 20413437
Reprod Toxicol. 2017 Mar;68:3-33
pubmed: 27760374
Biochem J. 1986 Sep 15;238(3):781-6
pubmed: 3800960
PLoS Comput Biol. 2017 Apr 13;13(4):e1005409
pubmed: 28406903
Vision Res. 2017 Oct;139:228-236
pubmed: 28545981
Circ Res. 2017 Feb 17;120(4):713-735
pubmed: 28209797
Bioinformatics. 2013 Aug 15;29(16):2009-16
pubmed: 23742984
Br J Ophthalmol. 2015 Jun;99(6):837-41
pubmed: 25488949
J Clin Med Res. 2020 May;12(5):293-299
pubmed: 32489504
Nutrients. 2020 Jul 31;12(8):
pubmed: 32751778
J Clin Invest. 1987 Jun;79(6):1713-9
pubmed: 3294899
Brief Bioinform. 2019 Nov 27;20(6):1957-1971
pubmed: 29304189
BMJ Open Diabetes Res Care. 2015 Jun 30;3(1):e000097
pubmed: 26157584
BMC Bioinformatics. 2011 Mar 17;12:77
pubmed: 21414208
Circ Res. 2020 Jan 3;126(1):60-74
pubmed: 31698999
Pediatr Clin North Am. 2018 Apr;65(2):179-208
pubmed: 29502909
J Am Coll Cardiol. 2004 Dec 7;44(11):2137-41
pubmed: 15582310
J Diabetes Complications. 2019 Oct;33(10):107383
pubmed: 31371129
J Inherit Metab Dis. 2018 May;41(3):355-366
pubmed: 29536203
J Inherit Metab Dis. 2018 May;41(3):337-353
pubmed: 29453510
Int J Mol Sci. 2020 Feb 11;21(4):
pubmed: 32054038
Bioanalysis. 2016 Jul;8(14):1509-32
pubmed: 27323646
Lipids Health Dis. 2020 Nov 19;19(1):241
pubmed: 33213461
Diabetol Int. 2020 Jul 25;11(3):224-239
pubmed: 32802703
BMC Syst Biol. 2012 Aug 29;6:114
pubmed: 22929619
Nutr Diabetes. 2020 Apr 27;10(1):13
pubmed: 32341356
Biochem J. 1925;19(2):338-9
pubmed: 16743508
Nutrients. 2020 Jul 10;12(7):
pubmed: 32664350
Biotechnol Prog. 1999 May-Jun;15(3):296-303
pubmed: 10356246