Defining the relationship between rheumatoid arthritis, multimorbidity and adverse health-related outcomes: a precision medicine approach

Problem

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by joint pain, stiffness and swelling. Multimorbidity (i.e. the presence of two or more long-term conditions in an individual) is highly prevalent among patients with RA. Despite therapeutic advances improving the clinical outcomes and long-term prognosis of RA, multimorbidity can cause additional complications in RA patients, with the effects of multimorbidity on mortality and adverse health outcomes in RA relatively unknown. While objective markers of inflammation are central in monitoring RA disease activity, subjective factors relating to patient wellbeing may be of greater relative importance when assessing the impact of co-existing physical and mental health morbidities in RA. This impact will vary by individual depending on biological, lifestyle and environmental factors and thus it will be important to explore the relationship between RA, multimorbidity and mortality/adverse health-related outcomes. This work aims to address this research gap.

The key research questions are:

1. What is known about the effect of multimorbidity on mortality/adverse health-related outcomes in people with RA?

2. How do demographic, lifestyle factors, morbidity measures and routine clinical blood tests mediate the relationship between individuals with RA and mortality/adverse health-related outcomes?

3. Is it possible to develop a predictive algorithm to identify individuals with RA and multimorbidity at high risk of adverse outcome?

Approach

This project consists of two main parts: a systematic review and quantitative analysis of three separate cohorts (UK Biobank, SERA and rheumatology cohorts in the Karolinksa Institute, KI). The UK Biobank is a general population cohort consisting of data collected from over 500,000 adult participants where 1.3% report RA. SERA (Scottish Early Rheumatoid Arthritis) is a national inception cohort comprising over 1,000 adults with newly diagnosed RA or undifferentiated arthritis in Scotland and combines patient- and clinician-reported data. Access to large RA inception, disease and treatment cohorts will also be achieved through collaboration with KI. The aim of the systematic review is to appraise existing literature to delineate the relationship between RA, multimorbidity and mortality/adverse health-related outcomes. Risk stratification models will be developed using population health and data science approaches, including in silico-type modelling. These will be developed using data from patients with established RA from UK Biobank and KI cohorts, and tested in separate inception cohorts for RA (SERA and KI) to compare predicted to observed outcomes.

Findings

Systematic review in progress.

Consequences

Without a defined relationship between RA and multimorbidity, it is difficult to develop and implement standardised treatment advice and guidelines that are inclusive to multimorbid RA patients. Precision medicine methods may be used to explore the risk stratification of patients with RA and multimorbidity; helping to identify those at increased risk of experiencing adverse clinical and health-related outcomes as a result of multimorbidity.