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

> **NIH NIH R21** · RESEARCH INST NATIONWIDE CHILDREN'S HOSP · 2020 · $190,000

## Abstract

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 direc...

## Key facts

- **NIH application ID:** 9999582
- **Project number:** 5R21EB026518-03
- **Recipient organization:** RESEARCH INST NATIONWIDE CHILDREN'S HOSP
- **Principal Investigator:** Aaron J Trask
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $190,000
- **Award type:** 5
- **Project period:** 2018-09-01 → 2022-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9999582

## Citation

> US National Institutes of Health, RePORTER application 9999582, Novel Non-Invasive Coronary Flow Patterning to Predict Early Coronary Microvascular Disease (5R21EB026518-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9999582. Licensed CC0.

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