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

项目摘要 众所周知，冠状动脉微血管疾病（CMD）难以无创诊断，目前 评估CMD的方法仅利用冠状动脉流动模式的峰值速度。虽然新的成像 心脏磁共振成像（MRI）等技术已改善了对冠状动脉的评估， 由于冠状动脉血流灌注，目前还没有非侵入性的方法， 完整的心动周期，以明确评估和预测CMD的发展。 冠状动脉血流量（CBF）反映了冠状动脉微循环中的流量总和，我们的实验室 开始在不同的血流和疾病条件（例如2型糖尿病）下利用完整的CBF模式， 确定它是否可能隐藏导致CMD早期检测的新线索。我们的过去和初步的 数据表明，2型糖尿病（T2DM）和代谢综合征（MetS）中CMD均早发 发生在大血管并发症发作之前，其特征是血流 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/生物力学与我们新定义的 冠状动脉血流模式的特点，其中一些是独特的正常或糖尿病小鼠。我们已初步 利用这些CBF特征，在存在和不存在其他因素（如心脏功能）的情况下， 克里斯托弗·巴特利特博士和威廉·雷博士合作的一个数学模型， 6个简单的因素可以预测正常与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 和它的生理调节剂（例如冠状动脉微血管结构/功能/生物力学，心脏 功能等），可能有助于直接诊断早期CMD。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 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，假设将通过解决两个具体目标进行测试：1）确定 正常和T2DM中独特的时间依赖性CBF模式是否由CRM的组合决定， 重塑和生物力学，冠状动脉血流模式动力学和心脏功能，允许 开发计算模型以准确预测CMD; 2）确定再现性， 机器学习模型在饮食诱导的肥胖/糖尿病小鼠模型中预测CMD的稳健性。 如果成功，这些研究将是第一个同时检查CRM，CBF和心脏的影响。 冠状动脉血流的不同模式的结构/功能，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的 CMD的直接非侵入性诊断用于预防和/或治疗心脏病。

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.