1. Stevens Institute of Technology Receives NIH Grant for Dynamic Tracer Kinetic Model to Detect Preclinical Diabetic Retinopathy

    Stevens Institute of Technology Receives NIH Grant for Dynamic Tracer Kinetic Model to Detect Preclinical Diabetic Retinopathy

    Stevens Institute of Technology Received a 2022 NIH Grant for $504,051 for Dynamic Tracer Kinetic Model to Detect Preclinical Diabetic Retinopathy (DR). The principal investigator is Jennifer Kang-Mieler. Below is a summary of the proposed study.

     

    Diabetic retinopathy (DR) is one of the most common complications associated with diabetes. Detection of clinical DR signs can take several years from the onset of diabetes; hence, the long preclinical phase should provide a window to apply interventions that can slow or prevent progression to clinical endpoint (mild to severe visual impairment). In fact, early detection and treatment of DR can prevent more than 90% of vision loss. However, the current unmet clinical challenge is finding an appropriate tool or technology to detect preclinical signs (biomarkers) of DR. Since the retinal vessels are early and prevalent targets of diabetic damage, sensitive identifiers of structural and functional blood vessel changes hold great potential as biomarkers. Recent advances in retinal imaging technology have allowed a better visualization of vessel characteristics. Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) and OCT angiography (OCTA) studies recently suggested that there may be a transitional vascular remodeling during the preclinical phase in diabetic patients. Though the main benefit of these technologies is the non-invasive nature of data acquisition, there are limitations (e.g., long scan times, limited field-of-view, motion artifacts and need for an expert operator) that prevent these technologies to be effective preclinical detection tools to be used in a clinical setting. Therefore, there is a great need for enhanced detection sensitivity and quantitative means to analyze the early preclinical vasculature changes that can be readily translated into clinical practice. To address this critical unmet clinical need, we have developed a novel dynamic tracer kinetic model to measure quantitatively vascular permeability and blood flow changes based on fluorescein video-angiography (FVA). The approach is immediately translatable to FVA data collected in patients as demonstrated by our preliminary data. In this proposal, we will demonstrate that our dynamic tracer kinetic model can detect preclinical DR with a higher sensitivity and specificity than other retinal imaging modalities such as OCTA and AOSLO. Specific Aim 1 will optimize/validate the retinal vascular permeability and blood flow measurements against gold standard techniques of permeability (Evans-blue) and blood flow (microspheres). Specific Aim 2 will demonstrate that longitudinal preclinical changes in the retinal vascular permeability and blood flow detected by our model will occur before clinical retinopathy in diabetic rodent model. Specific Aim 3 will characterize longitudinal changes in retinal vascular permeability and blood flow in both normal subjects and diabetic patients. Specific Aim 4 will demonstrate higher sensitivity of preclinical DR detection by the dynamic tracer kinetic model over optical coherence tomography angiography (OCTA) and adaptive optics scanning laser ophthalmoscopy (AOSLO) in diabetic patients without clinical retinopathy (DMnoDR).

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