1. Johns Hopkins University Receives NIH Grant for Imaging neurodegeneration in multiple sclerosis

    Johns Hopkins University Receives NIH Grant for Imaging neurodegeneration in multiple sclerosis

    Johns Hopkins University Received a 2022 NIH Grant for $536,829 for Imaging neurodegeneration in multiple sclerosis. The principal investigator is Peter Calabresi. Below is a summary of the proposed work.

    One of the significant challenges facing treatment of people with multiple sclerosis (MS) is determining their individual likelihood of progression, as this information would significantly influence the type of therapy selected. Thus, developing specific tools to monitor and predict progression is critical to better manage patient care and to understand mechanisms of disease. We have been developing a multi-faceted approach to more readily monitor (through imaging) and predict (through both imaging and genetic analysis) disease progression in a real-time fashion. In the past cycle of this grant we demonstrated the utility of high resolution spectral domain optical coherence tomography (SD-OCT) and magnetic resonance imaging (3T MRI) in estimating disease burden in different CNS compartments. We showed that retinal degeneration occurs throughout the disease course and mirrors grey matter compartment atrophy in the cerebrum. A critical finding validating the clinical utility of this approach was that in a multicenter analysis of pooled data, a single OCT at baseline predicted risk of disability progression at 5 years of follow up. As MS is thought to have a strong genetic component, we sought to investigate whether there was an underlying genetic predisposition towards progression, which was made possible by the ability to utilize OCT in a real-time fashion to monitor degeneration and correlate with clinical outcome. We thus expanded the imaging study to include a genetic component in which we conducted a gene array to evaluate genetic variation among people with heterogeneous courses of MS and have preliminarily found that several gene variants in network pathways appear to be associated with more rapid rates of retinal neurodegeneration. The large data set that will be generated from these studies will also allow a corollary analysis in which we can begin to develop a risk profile model in which other population characteristics known to be associated with disease such as sex and ethnicity can be incorporated. The central hypotheses of the proposed studies are; that retinal ganglion layer thickness, thalamic and GM volumes predict 10 year disability across MS subtypes, that patients with high genetic load for gene variants in specific network pathways undergo faster neurodegeneration, and that combinations of OCT, MRI and genetic load measures may be used to develop clinically meaningful individual predictive scores for precision medicine. Aim 1: To determine whether baseline retinal ganglion layer thickness and thalamic and GM volumes predict 10 year disease outcomes. Aim 2: To determine whether genetic variation, sex and ethnicity influence rates of GCIP, thalamic, GM atrophy, and disability accumulation. Aim 3: To develop an algorithm disease progression model to predict disease outcome.

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