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

IF 3.4 3区医学 Q1 MEDICAL INFORMATICS

Health Information Science and Systems Pub Date : 2023-03-29 eCollection Date: 2023-12-01 DOI:10.1007/s13755-023-00218-x

Arsen Batagov, Rinkoo Dalan, Andrew Wu, Wenbin Lai, Colin S Tan, Frank Eisenhaber

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引用次数: 1

Abstract

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.

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广义代谢通量分析框架为2型糖尿病患者的眼科并发症提供了基于机制的预测。

慢性代谢性疾病是由个体一生中积累的生物分子途径和基因网络引起的代谢通量变化引起的。虽然临床和生物化学图谱只是患者健康状况的实时快照，但需要生物分子过程的病理紊乱的有效计算模型来实现对疾病进展的个性化机制见解。在这里，我们描述了广义代谢通量分析（GMFA）来解决这一差距。将单个代谢物/通量适当地分组到池中简化了对所产生的更粗颗粒网络的分析。我们还将非代谢临床模式映射到具有额外边缘的网络上。不是使用时间坐标，而是将系统状态（代谢物浓度和通量）量化为广义程度变量（广义代谢物空间中的坐标）的函数，广义程度变量表示系统沿其进化路径的坐标，并评估该路径上任何两个状态之间的变化程度。我们应用GMFA分析了来自两个队列的2型糖尿病（T2DM）患者：来自新加坡的EVAS（289名患者）和来自美国的NHANES（517名）。构建了个性化的系统生物学模型（数字双胞胎）。我们从单独参数化的代谢网络中推导出疾病动力学，并预测了代谢健康状态的进化路径。对于每个患者，我们获得了疾病动力学的个体描述，并预测了代谢健康状态的进化路径。我们的预测模型在基线时识别表型并预测T2DM患者在基线后3年内糖尿病视网膜病变和白内障进展的未来发展方面，ROC-AUC在0.79-0.95范围内（敏感性80-92%，特异性62-94%）。GMFA方法是实现最终目标的一步，即开发基于系统生物学的诊断实用预测计算模型。该工具有可能用于医疗实践中的慢性病管理。补充信息：在线版本包含补充材料，可访问10.1007/s13755-023-00218-x。

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来源期刊

Health Information Science and Systems MEDICAL INFORMATICS-

CiteScore

11.30

自引率

5.00%

发文量

期刊介绍： Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.