7M-Multiomic Multiscale and Multifidelity Modeling via Machine Learning: Application to Diagnosis and to Macrovascular and Microvascular complications in NAFLD.

在工业化国家，非酒精性脂肪性肝病（NAFLD）在普通人群中的患病率为30%，在肥胖人群中为40-70%，在糖尿病人群中为60-90%。NAFLD的特点是存在非酒精性脂肪性肝（NAFL），可发展为脂肪性肝炎（NASH）、纤维化和肝硬化。NAFLD患者的生存率由于肝脏和心血管相关死亡率较高而降低。因此，NAFLD的早期诊断及心血管危险因素的评估和治疗非常重要。迄今为止，肝活检被认为是诊断该疾病的金标准，因为非侵入性生物标志物和评分显示敏感性或特异性较低。此外，与NAFLD心血管死亡率增加相关的机制尚未得到充分的确定和描述。由于方法上的巨大差异，NAFLD是否以及如何增加血管闭塞的风险尚不清楚。该提案的目的是开发一种简化的算法，以最小的必要参数诊断NAFL和NASH，其灵敏度和特异性为bbbb90 % (Aim 1)，研究和比较NAFL和NASH的生物力学和血液流变学（Aim 2）以及大血管和微血管疾病的发病机制（Aim 3）。对于Aim 1，将对n=160例肝脏活检患者（无NAFLD的瘦和超重/肥胖患者，NAFL或NASH的超重/肥胖患者）进行首次多组学分析，包括血清蛋白质组学、代谢组学、脂质组学和糖组学。数据将用先进的计算数学模型进行分析，以定义预测NAFL或NASH的方程。在第二阶段和第三阶段，将建立一个多尺度、多保真度的NAFLD生物力学模型。在离体微观水平，将评估红细胞（RBC）变形能力和rouleau形成，血小板大小和rouleau形成以及血小板-红细胞相互作用/聚集。在中宏水平，将研究离体血液粘度、血浆粘度和血栓形成的变化。在宏观层面上，将在类似于患者特定大血管的3d打印通道中研究血流、血栓形成和稳定性。为了形成3d打印的患者特异性通道，将使用颈动脉超声和24小时心血管评估系统的体内数据来模拟患者特异性动脉粥样硬化和动脉僵硬。在多尺度分析的所有步骤中，通过深度高斯过程的多保真度建模将被用于发现“未知”的功能关系和关键途径，并通过体内数据，离体数据和模拟协同连接尺度。综上所述，本研究旨在显著改善NAFLD的无创诊断方法以及与该疾病相关的血管疾病风险评估方法。

Nonalcoholic fatty liver disease (NAFLD) has a prevalence of 30% in the general population, 40-70% in the obese and 60-90% in the diabetic population in industrialized countries. NAFLD is characterized by the presence of non-alcoholic fatty liver (NAFL) that can progress to steatohepatitis (NASH), fibrosis and cirrhosis. The survival of patients with NAFLD is reduced due to higher rates of hepatic and cardiovascular-related death. For this reason, the early diagnosis of NAFLD and the evaluation and treatment of the cardiovascular risk factors are very important. To date, liver biopsy is considered the gold-standard for the diagnosis of the disease, since non-invasive biomarkers and scores demonstrate low sensitivity or specificity. Additionally, the mechanisms associated with the increased cardiovascular mortality in NAFLD have not been adequately identified and described. Due to big differences in methodological approaches, it is still not clear whether and how NAFLD can increase the risk of vascular occlusion. Aims of the proposal are to develop a simplified algorithm with the minimum necessary parameters to diagnose NAFL, NASH with a sensitivity and specificity >90% (Aim 1), to investigate and compare biomechanics and rheology of blood (Aim 2) as well as the pathogenesis of macro- and microvascular disease (Aim 3) in NAFL and NASH. For Aim 1, the first-ever multiomic analysis including serum proteomics, metabolomics, lipidomics and glycomics in n=160 subjects with liver-biopsy (lean and overweight/obese subjects without NAFLD, overweight/obese patients with NAFL or NASH) will be performed. Data will be analyzed with advanced computational mathematical models to define an equation for the prediction of NAFL or NASH. For Aim 2 and Aim 3, a multiscale, multifidelity biomechanistic model for NAFLD will be developed. At the micro-level ex vivo, erythrocyte (RBC) deformability and rouleau formation, platelet size and rouleau formation and platelet-RBC interaction/aggregation will be evaluated. At the meso-macro level ex vivo changes in blood viscosity, plasma viscosity and thrombus formation will be investigated. At the macro-level, blood flow, thrombus formation and stability will be investigated in 3D-printed channels resembling patient-specific large vessels. For the formation of 3D-printed patient-specific channels in vivo data from carotid artery sonography and 24h-cardiovascular assessment system to imitate patient-specific atherosclerosis and arterial stiffness will be used. In all steps of the multiscale analysis, multifidelity modeling via deep Gaussian processes will be employed to discover "unknown" functional relationships and critical pathways and to bridge scales, synergistically from in vivo data, ex vivo data and simulations. To summarize, the current study aims to significantly improve the available methods for the non-invasive diagnosis of NAFLD as well as for the evaluation of vascular disease risk related to the disease.