Rheumatoid arthritis (RA) is an inflammatory disease in joints and tendons. If not treated, it may cause serious damage of the joints. It is large individual differences in disease progression as well as efficacy of medication. Emerging data suggest that RA is not one disease but comprised by subsets with distinct aetiologies, which may explain the difficulties in predicting disease course, treatment response, and long-term outcomes. So far we treat by tradition and try different treatments in a rather random fashion and often too late, despite the increasing number of effective medicines that have become available in routine care. This is why the present study is important to explore and develop prediction tools that can help to give the patient the optimal medication.
The goal of the NORA study is by use of modern knowledge regarding genetics, biomarkers, new ways of using clinical and patient-reported data as well as use of registrars, to develop methods that on a patient level can help to identify the correct choice of medication.
The study is based on a collaboration between strong scientific centres in the Nordic countries, including groups with high expertise on laboratory work and digital competence. The study is led by the Karolinska Institute in Stockholm.
It is a comprehensive study using several existing patient cohorts in the Nordic countries, where large amounts of clinical data and biological material are collected, and where advanced technical and digital analyses will be used to develop methods for personalized medicine.
Effective medication for RA patients is expensive and may cause side effects. Thus, development of personalized medicine will be of high importance for both patients and the society.
The study is divided into 8 work packages (WP). WP1 is now completed, and our group is included in moste of the WP. In addition, we are responsible for WP5, with the main goal to validate the predictors found in the previous WPs. We have now a collaboration with the study groups of the recently completed studies NORDSTAR (a Nordic study on early RA patients) and DANACT (a Danish study on early RA). We will explore the predictors found in the NORA study (genetical, laboratory, imagaging, patient reported) in the NORDSTAR and DANACT study to verify the findings in the NORA study on predictive biomarkers.
Rheumatoid arthritis (RA, life-time risk around 2%) entails a considerable burden for affected individuals (pain, reduced life-span, reduced function, reduced quality of life), and delayed or ineffective treatment leads to joint destruction, co-morbidities, and increased mortality. RA demonstrates a striking heterogeneity in terms of clinical presentation, response to treatment, and clinical outcomes, with emerging data suggesting RA to be a subsets with distinct etiologies. So far we treat by tradition and try different treatments in a rather random fashion and often too late.
The goal of this project is to develop a personalized medicine approach to the management of Rheumatoid Arthritis (RA), both by the development of new prediction tools and by digital tools to bring these now insights to patients and to health-care.
Our approach builds on our strong collaboration within Nordic Rheumatology including work with data sharing across the Nordic countries, and a complementary public-private partner constellation that spans all the necessary competences and infrastructures. The novelty in our approach includes the availability and analyses of data across different domains (genomics, biomarkers, clinical data, patient-reported data, register-linkage data) that so far have not been analyzed together, new biomarker assays, the inclusion of patient-centric outcomes (e.g., pain) rather than composite and insensitive outcome metrics. In addition, our constellation of partners will also develop, validate and disseminate these new insights into improvement of therapy directly to patients and health care via the development of digital tools.
The anticipated outcomes of our approach is better and earlier treatment of RA paired with the development of products (diagnostics and digital companion tools) that can be used for cost-efficient improvement of treatment results for patients, and thus become attractive commercial products as well.