Novel Non-Invasive Coronary Flow Patterning to Predict Early Coronary Microvascular Disease

新型非侵入性冠状动脉血流模式可预测早期冠状动脉微血管疾病

• 批准号: 9769734
• 负责人: Aaron J Trask
• 金额: $ 22.8万
• 依托单位: RESEARCH INST NATIONWIDE CHILDREN'S HOSP
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9769734
• 关键词: Address; Age; Artificial Intelligence; Biomechanics; Blood flow; Cardiac; Collaborations; Computer Simulation; Coronary; Coronary Arteriosclerosis; Coronary artery; Data; Development; Diabetes Mellitus; Diabetic mouse; Diet; Disease; Doppler Echocardiography; Early Diagnosis; Echocardiography; Elements; Heart Diseases; Hyperemia; Hypertension; Imaging Techniques; Impairment; Interdisciplinary Study; Laboratories; Machine Learning; Magnetic Resonance Imaging; Measures; Metabolic syndrome; Methods; Microcirculation; Microvascular Dysfunction; Modeling; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Pattern; Physiological; Publications; Reproducibility; Resistance; Structure; Testing; Time; base; coronary perfusion; db/db mouse; diabetic; disorder prevention; early onset; experimental study; heart function; improved; innovation; interdisciplinary approach; macrovascular disease; mathematical model; noninvasive diagnosis; novel; pressure; prevent

PROJECT SUMMARY Coronary microvascular disease (CMD) is notoriously difficult to diagnose non-invasively, and current methods of assessing CMD utilize only the peak velocity of the coronary flow pattern. While new imaging techniques such as cardiac magnetic resonance imaging (MRI) have improved the assessment coronary perfusion, there are currently no non-invasive methods that incorporate the coronary flow pattern over a complete cardiac cycle to definitively assess and predict the development of CMD. Coronary blood flow (CBF) reflects the summation of flow in the coronary microcirculation, and our lab has begun to harness the full CBF pattern under varying flow and disease conditions (e.g. type 2 diabetes) to determine whether it might harbor novel clues leading to the early detection of CMD. Our past and preliminary data indicate an early onset of CMD in both type 2 diabetes mellitus (T2DM) and metabolic syndrome (MetS) that occurs prior to the onset of macrovascular complications and that are characterized by blood flow impairments and alterations in coronary resistance microvessel (CRM) structure, function, and biomechanics. Our data also uncovered innovative correlations between CRM structure/biomechanics and our newly-defined features of the coronary flow pattern, some of which were unique to normal or diabetic mice. We have initially utilized these CBF features, in the presence and absence of other factors such as cardiac function, to develop a mathematical model in collaboration with Drs. Christopher Bartlett and William Ray that to date demonstrated that 6 simple factors can predict a normal vs. diabetic coronary flow pattern with 85% predictive accuracy. Utilizing a multidisciplinary approach, these preliminary data strongly suggest that the coronary flow pattern and physiological modulators of it (e.g. coronary micovascular structure/function/biomechanics, cardiac function, etc), may be useful in directly diagnosing early CMD. Therefore, we hypothesize that dissecting the elements that influence coronary flow patterning will be critical determinants in the direct assessment of coronary microvascular disease using computational modeling. Using our previous publications and our preliminary data as guides, the hypothesis will be tested by addressing two specific aims: 1) Determine whether unique time-dependent CBF patterning in normal and T2DM is dictated by a combination of CRM remodeling and biomechanics, coronary flow pattern dynamics, and cardiac function, permitting the development of a computational model to accurately predict CMD; 2) Determine the reproducibility and robustness of the machine learning model in predicting CMD in a diet-induced obesity/diabetes mouse model. If successful, these studies will be the first to simultaneously examine the influence of CRMs, CBF, and cardiac structure/function on the distinct pattern of coronary flow, and it will determine whether a mathematical model may be useful in establishing a direct assessment of CMD to eventually enable clinicians to conduct a more direct non-invasive diagnosis of CMD for the prevention and/or treatment of heart disease.

项目总结 冠状动脉微血管疾病(CMD)是出了名的难以非侵入性诊断，目前 评估CMD的方法仅使用冠脉血流模式的峰值速度。虽然新的成像技术 心脏磁共振成像(MRI)等技术改进了对冠状动脉的评估 灌注，目前还没有非侵入性的方法来结合冠状动脉血流模式 完整的心动周期以明确评估和预测CMD的发展。 冠脉血流量(CBF)反映了冠脉微循环中血流的总和，我们实验室已经 开始在不同的血流和疾病条件下(例如2型糖尿病)利用完整的CBF模式来 确定它是否可能蕴藏着导致早期发现CMD的新线索。我们的过去和初步 数据显示，在2型糖尿病(T2 DM)和代谢综合征(METS)中，CMD都是较早发病的 发生在大血管并发症发生之前，以血流为特征 冠状动脉阻力微血管(CRM)结构、功能和生物力学的损伤和改变。 我们的数据还揭示了CRM结构/生物力学和我们的新定义之间的创新关联 冠脉血流模式的特征，其中一些是正常或糖尿病小鼠所独有的。我们已经初步完成了 利用这些CBF特征，在存在和不存在其他因素(如心功能)的情况下，开发 一个与克里斯托弗·巴特利特博士和威廉·雷博士合作的数学模型 这6个简单因素可以预测正常和糖尿病冠状动脉血流模式，预测准确率为85%。 利用多学科方法，这些初步数据强烈表明，冠状动脉血流模式 以及它的生理调节因子(例如冠状动脉微血管结构/功能/生物力学、心脏 功能等)，可用于早期CMD的直接诊断。因此，我们假设解剖 影响冠状动脉血流模式的因素将是直接评估的关键决定因素 使用计算机建模进行冠状动脉微血管疾病的研究。使用我们以前的出版物和 我们的初步数据作为指导，将通过解决两个具体目标来检验假设：1)确定 正常和T2 DM患者独特的随时间变化的CBF模式是否由CRM的组合决定 重塑和生物力学、冠脉血流动力学和心功能，允许 开发精确预测CMD的计算模型；2)确定重复性和 机器学习模型在饮食诱导肥胖/糖尿病小鼠模型中预测CMD的稳健性。 如果成功，这些研究将是第一次同时检查CRM、CBF和心脏的影响 结构/功能对冠状动脉血流的不同模式，它将决定是否有一个数学模型 可能有助于建立对CMD的直接评估，最终使临床医生能够进行更多 用于预防和/或治疗心脏病的CMD的直接无创诊断。