PMID- 37008895
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231217
IS  - 2047-2501 (Print)
IS  - 2047-2501 (Electronic)
IS  - 2047-2501 (Linking)
VI  - 11
IP  - 1
DP  - 2023 Dec
TI  - Generalized metabolic flux analysis framework provides mechanism-based 
      predictions of ophthalmic complications in type 2 diabetes patients.
PG  - 18
LID - 10.1007/s13755-023-00218-x [doi]
LID - 18
AB  - 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. SUPPLEMENTARY INFORMATION: The online version contains 
      supplementary material available at 10.1007/s13755-023-00218-x.
CI  - (c) The Author(s) 2023.
FAU - Batagov, Arsen
AU  - Batagov A
AUID- ORCID: 0000-0002-9620-079X
AD  - Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore.
FAU - Dalan, Rinkoo
AU  - Dalan R
AD  - Department of Endocrinology, Tan Tock Seng Hospital, Singapore, Singapore. GRID: 
      grid.240988.f. ISNI: 0000 0001 0298 8161
AD  - Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 
      Singapore. GRID: grid.59025.3b. ISNI: 0000 0001 2224 0361
FAU - Wu, Andrew
AU  - Wu A
AD  - Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore.
FAU - Lai, Wenbin
AU  - Lai W
AD  - Mesh Bio Pte. Ltd., 10 Anson Rd, #22-02, 079903 Singapore, Singapore.
FAU - Tan, Colin S
AU  - Tan CS
AD  - Fundus Image Reading Center, National Healthcare Group Eye Institute, Singapore, 
      Singapore. GRID: grid.466910.c. ISNI: 0000 0004 0451 6215
AD  - Tan Tock Seng Hospital, National Healthcare Group Eye Institute, Singapore, 
      Singapore. GRID: grid.466910.c. ISNI: 0000 0004 0451 6215
AD  - Duke-NUS Medical School, Singapore, Singapore. GRID: grid.428397.3. ISNI: 0000 
      0004 0385 0924
FAU - Eisenhaber, Frank
AU  - Eisenhaber F
AD  - Bioinformatics Institute (BII), Agency for Science, Technology and Research 
      (ASTAR), Singapore, Singapore. GRID: grid.185448.4. ISNI: 0000 0004 0637 0221
AD  - Genome Institute of Singapore (GIS), Agency for Science, Technology and Research 
      (ASTAR), Singapore, Singapore. GRID: grid.185448.4. ISNI: 0000 0004 0637 0221
AD  - School of Biological Science (SBS), Nanyang Technological University, Singapore, 
      Singapore. GRID: grid.59025.3b. ISNI: 0000 0001 2224 0361
LA  - eng
PT  - Journal Article
DEP - 20230329
PL  - England
TA  - Health Inf Sci Syst
JT  - Health information science and systems
JID - 101638060
PMC - PMC10060506
OTO - NOTNLM
OT  - Cataract
OT  - Diabetes
OT  - Diabetic complications
OT  - Digital twin
OT  - Metabolic flux analysis
OT  - Retinopathy
COIS- 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.
EDAT- 2023/04/04 06:00
MHDA- 2023/04/04 06:01
PMCR- 2023/03/29
CRDT- 2023/04/03 04:20
PHST- 2022/03/08 00:00 [received]
PHST- 2023/02/19 00:00 [accepted]
PHST- 2023/04/04 06:01 [medline]
PHST- 2023/04/03 04:20 [entrez]
PHST- 2023/04/04 06:00 [pubmed]
PHST- 2023/03/29 00:00 [pmc-release]
AID - 218 [pii]
AID - 10.1007/s13755-023-00218-x [doi]
PST - epublish
SO  - Health Inf Sci Syst. 2023 Mar 29;11(1):18. doi: 10.1007/s13755-023-00218-x. 
      eCollection 2023 Dec.