Metabolomics profiling implicates altered lipid metabolism in neuromyelitis optica spectrum disorder
Elina Misicka1, Farren Briggs1
1Case Western Reserve University
Objective:

To identify serum biomarkers predictive of neuromyelitis optica spectrum disorder (NMOSD).

Background:

NMOSD is a rare demyelinating, autoimmune disease. The autoimmune target is the astrocyte water channel aquaoporin-4, and its antibodies (APQ4-IgG) are highly specific for NMOSD, though 10-27% of affected persons will be APQ4-IgG negative. Thus, some affected persons may experience highly variable diagnostic delays, including misdiagnoses. There are opportunities to identify novel NMOSD-specific biomarkers by comparing metabolomic profiles in NMOSD to multiple sclerosis (MS) and unaffected controls. 

Design/Methods:

This study was based on serum samples available through the Accelerated Cure Project, from 24 NMOSD (50% APQ4-IgG positive), 70 relapsing remitting (RR) MS, and 83 unaffected controls (UC) who self-identified as non-Hispanic whites. All NMOSD+RRMS cases were immunomodulatory therapy naïve/free (>90 days), <5 years from first symptom, and <2 years from diagnosis. Untargeted metabolomic profiles were generated, and after quality control and normalized/standardization, there were 952 named biochemical traits for analysis. A supervised machine-learning algorithm, Random forests, identified biochemical traits informative for NMOSD in comparison to MS+UC. Multivariable regression analyses characterized associations adjusting for age, sex, smoking status, and body mass index. Receiver operator curves (ROC) evaluated the predictive capacity of top-ranking metabolites.

Results:

Random forests determined 4 metabolites as informative for NMOSD vs MS+UC. They included a ceramide and 3 monoacylglycerols. They did not differ between APQ4-IgG positive and negative samples (p>0.2) nor between MS and UC samples (p>0.25). The metabolites were >1 standard deviation higher in NMOSD compared to MS+UC even with adjustment for potential confounders (p: 1x10-5 to 2x10-10), and were highly predictive of NMOSD status (area under ROC >80%) in comparison to MS+UC and versus UC alone. The full parameterized multivariable model was highly predictive (area under ROC=91.5%).

Conclusions:

We observed a serum biosignature highly predictive of NMOSD, implicating sphingomyelin and triglyceride metabolic processes.

10.1212/WNL.0000000000202989