# Characterizing the serum metabolome in multiple sclerosis

> **NIH NIH R01** · CASE WESTERN RESERVE UNIVERSITY · 2022 · $603,729

## Abstract

PROJECT SUMMARY AND ABSTRACT
 Within the last decade, we have made great strides in our understanding of the mechanisms underlying
multiple sclerosis (MS) risk and progression, however much of the variation remains unexplained. We have
achieved significant reductions in the time to diagnosis and we have improved diagnostic sensitivity, however
specificity is not ideal. Further, most of the FDA-approved MS-specific immunomodulatory therapies (IMTs) focus
on the inflammatory disease component in the relapsing phase and have little effect on improving outcomes
once a patient enters the progressive phase. The challenge for drug trials is the lack biomarkers to detect and
monitor MS progression. The objectives of the current application are: 1. To identify and characterize
biomarkers that discriminate MS and from other central nervous system inflammatory demyelinating diseases
(CNSIDDs) and non-CNSIDD controls, and 2. To identify biomarkers of disease activity and biomarkers that
distinguish relapsing from progressive forms of MS. We propose a multi-stage analysis of pre-existing and well-
defined biological samples from two resources.
 Aim 1. Identify biochemical traits that discriminate MS from other CNSIDDs and healthy controls.
Supervised machine learning and classification models will identify a metabolic signature discriminating MS from
other CNSIDDs and healthy controls (HCs) in two cohorts. In the 1st cohort, MS patients who are early in their
diagnosis (≤ 2 years) and IMT naïve/free will be compared to HCs and other CNSIDD cases. Discriminating
metabolites will be tested for replication in a 2nd cohort comparing similarly defined MS patients to HCs and other
CNSIDDs, and other autoimmune disease patients. We will determine the direction of the replicating MS-
metabolite associations using bidirectional genetic instrumental variable analyses. Aim 2. Identify biochemical
features of MS disease activity. We will identify metabolic variation corresponding to disease activity by
comparing IMT naïve/free patients within 2 years of diagnosis and with a recent relapse to those who have been
in remission for ≥3 months and to HCs using supervised classification in a discovery cohort followed by
replication analyses in a 2nd cohort. Aim 3. Identify biochemical traits that discriminate progressive from
relapsing MS. Supervised machine learning and classification models will identify metabolic patterns associated
with MS progression by comparing IMT naïve/free patients with relapsing forms of MS to progressive MS from
at a single academic specialty clinic. Aim 4. Identify metabolites that interact with HLA-DRB1*15:01 to
increase MS risk. In this exploratory aim we will identify gene-metabolite (GxM) interactions involving the
primary MS risk factor, HLA-DRB1*15:01. The encoded peptide is involved in antigen presentation and
effectively binds to many endogenous metabolites, suggesting a mechanism through which autoreactive T cells
may be activated. We will...

## Key facts

- **NIH application ID:** 10390352
- **Project number:** 5R01NS121928-02
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Farren B. S. Briggs
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $603,729
- **Award type:** 5
- **Project period:** 2021-04-15 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10390352

## Citation

> US National Institutes of Health, RePORTER application 10390352, Characterizing the serum metabolome in multiple sclerosis (5R01NS121928-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10390352. Licensed CC0.

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