Blood inflammatory proteins may be key predictor in NMOSD: Study
Levels of 6 proteins seen to predict risk of relapse, severe attacks
The circulating levels in the blood of six inflammatory proteins may help predict the risk of relapse and severe attacks in people with neuromyelitis optica spectrum disorder (NMOSD), a study from China reports.
Four of these inflammation-related proteins — FGF-23, DNER, GDNF, and SLAMF1 — were used to build a relapse prediction model, while two others, PD-L1 and MCP-2, were used to create a prediction model for attack severity. Both models outperformed the predictive performance of each protein alone, according to the researchers.
“Our findings not only lay the foundation for blood-based tests for predicting and monitoring the disease course of NMOSD but also provide future directions for research on disease mechanisms and therapeutic targets,” the scientists wrote.
The study, “Blood-based inflammatory protein biomarker panel for the prediction of relapse and severity in patients with neuromyelitis optica spectrum disorder: A prospective cohort study,” was published in the journal CNS Neuroscience & Therapeutics.
Seeking biomarkers for an earlier diagnosis in NMOSD
NMOSD is a progressive autoimmune disease in which the optic nerve, which transmits signals between the eye and the brain, and the spinal cord, becomes inflamed.
Typically, its diagnosis is achieved after the first symptoms appear. However, an earlier diagnosis would allow physicians to find and apply the best treatment to prevent further symptoms from progressing.
While blood testing to check for the presence of disease-causing autoantibodies — such as those that target the water channel protein aquaporin-4 (AQP4) — is a common approach, their levels do not correlate with clinical severity. Also, not all NMOSD patients will test positive for these autoantibodies.
As such, there’s a need for “easily accessible, noninvasive, and blood-based markers for the diagnosis and monitoring of disease progression in patients with NMOSD,” according to the researchers.
With this in mind, a team led by researchers from The First Hospital of China Medical University investigated whether the levels of circulating inflammation biomarkers correlated with relapse and attack severity in NMOSD.
The team first analyzed data from 30 patients with NMOSD, all women, who made up the so-called discovery group. A validation group, comprised of 20 patients, was later used when the researchers sought to replicate their findings.
The discovery group patients, who had a mean age of 47, were followed at the hospital between July 2020 and June 2023, and tracked for one year.
According to their clinical features, the patients were classified into different groups: Those who had relapsed were named to the recurrence group, while those who did not were in the nonrecurrence group. There also were different groups for those with severe attacks versus moderate/mild attacks. Severe attacks were based on a score of six or higher on the expanded disability status scale (EDSS), and moderate/mild attacks were determined by an EDSS of up to 5.5.
The researchers then examined the levels of 92 inflammatory proteins in the blood and used prediction models to assess how these correlated with each group. To confirm their findings, they tested the prediction models in the second group of patients, the validation group. Among those patients, the mean age was 48.25, and 95% were women.
In both groups, intravenous or into-the-vein use of the steroid methylprednisolone was the most commonly used treatment for acute attacks. Prednisone and mycophenolate mofetil (MMF, sold as CellCept) were most commonly used as a maintenance therapy by patients in both the discovery and validation groups.
Inflammatory proteins used in creating new prediction models
The analysis of circulation inflammatory proteins showed that seven of them were significantly different between the relapse group and the nonrelapse group, and another seven were significantly different between the severe attack group and the mild attack group.
All except one of the significantly different proteins in the relapse group showed a positive correlation — meaning the greater one, the greater the other — with NMOSD relapse. All proteins with a significantly distinct level in the severe group showed significant correlations with EDSS scores.
Among the relapse-related proteins, the FGF-23, DNER, GDNF, and SLAMF1 proteins showed a significant positive correlation with relapse number. In the case of severe disease, this correlation was seen for proteins PD-L1 and MCP-2 with the EDSS score.
[These blood] biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression.
The researchers then used the combination of FGF-23, DNER, GDNF, and SLAMF1 to create a relapse prediction model, and PD-L1 and MCP-2 for a prediction model of attack severity. Patients were then divided into low- and high-risk groups based on the optimal cutoff determined by the model.
First, the team confirmed that the predictive power of each model was better when compared with the prediction power based on each single protein.
According to the models, 87.5% of patients in the discovery high-risk group experienced a relapse. Meanwhile, the discovery severe attack risk group had a 55.56% severe attack rate. Similar findings were observed in the validation group, with 100% of high-risk patients experiencing a relapse and 100% a severe attack.
The model’s performance was confirmed by independent survival analysis in both the discovery and validation groups.
Overall, these blood “biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression,” the study concluded.
About the Author
