Generalized metabolic flux analysis framework provides mechanism-based predictions of ophthalmic complications in type 2 diabetes patients

• Published in: Health information science and systems Vol. 11; no. 1; p. 18
• Main Authors: Batagov, Arsen, Dalan, Rinkoo, Wu, Andrew, Lai, Wenbin, Tan, Colin S., Eisenhaber, Frank
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 29.03.2023; BioMed Central Ltd; Springer Nature B.V
• Subjects: Analysis; Bioinformatics; Biology; Chronic diseases; Complications and side effects; Computational Biology/Bioinformatics; Computer Science; Development and progression; Diabetes; Diabetes mellitus; Diabetic retinopathy; Digital twins; Disease; Evolution; Fluxes; Health Informatics; Information Systems and Communication Service; Medical research; Medicine, Experimental; Metabolic disorders; Metabolism; Metabolites; Prediction models; Type 2 diabetes; Diabetic complications; Cataract; Retinopathy; Diabetes; Digital twin; Metabolic flux analysis
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-023-00218-x

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.