LONDON—Research continues to advance in understanding the causes, prediction and management of the stages of early arthritis before full-blown clinical disease, and an expert highlighted some of the latest of these encouraging findings at the Annual Congress of the European League Against Rheumatism (EULAR) 2016.
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Many of the genetic and environmental risk factors are known, but what hasn’t been known is the stage at which they become relevant. Investigators are making progress on this question, said Karim Raza, MD, PhD, professor of clinical rheumatology at the University of Birmingham in the United Kingdom.
In one recent study, researchers found that the human leukocyte antigen (HLA) DRB1*13 alleles, known to be associated with protection from anti-citrullinated protein antibody (ACPA) positive RA, does not seem to be associated with much protection from the development of ACPA. So the relevance of this gene seems to be mostly at the level of those who are already ACPA positive, protecting against the development of RA itself.1
According to Dr. Raza, this could have a therapeutic impact. “If you can understand the mechanisms underlying this protection, then you can harness that and develop treatments for people who are ACPA positive to reduce the risk of transmission to RA,” he said.
Another study, looking at people at risk of RA, particularly first-degree relatives of those with RA, found that taking omega-3 fatty acid supplements was linked with a lower chance of being rheumatoid factor positive, among those who were positive for the shared epitope, a common RA risk factor.2
One study found that synovitis, tenosynovitis & bone marrow edema were predictive of development of clinical arthritis.
On the prediction front, researchers late last year published findings that adding ultrasound imaging variables to clinical variables helped improve the ability of a model to predict development of RA in people who are ACPA positive and have arthralgia.3 Other studies, focusing on lab values, such as adipokines, type 1 interferon and B cell signatures, and numbers of regulatory and naive T cells, all found that these factors can help with prediction as well.
“I think there’s increasing insight that by adding data from imaging and novel laboratory markers, we’ll be able to refine the accuracy of algorithms to predict RA development in individuals at risk,” Dr. Raza said.