Predicting Inflammatory Arthritis in At-Risk Persons: Development of Scores for Risk Stratification

BACKGROUND

Inflammatory arthritis (IA) is an immune-related condition defined by the presence of clinical synovitis. Its most common form is rheumatoid arthritis.

OBJECTIVE

To develop scores for predicting IA in at-risk persons using multidimensional biomarkers.

DESIGN

Prospective observational cohort study.

SETTING

Single-center, Leeds, United Kingdom.

PARTICIPANTS

Persons with new musculoskeletal symptoms, a positive test result for anticitrullinated protein antibodies, and no clinical synovitis and followed for 48 weeks or more or until IA occurred.

MEASUREMENTS

A simple score was developed using logistic regression, and a comprehensive score was developed using the least absolute shrinkage and selection operator Cox proportional hazards regression. Internal validation with bootstrapping was estimated, and a decision curve analysis was done.

RESULTS

Of 455 participants, 32.5% (148 of 455) developed IA, and 15.4% (70 of 455) developed it within 1 year. The simple score identified 249 low-risk participants with a false negative rate of 5% (and 206 high-risk participants with a false-positive rate of 72%). The comprehensive score identified 119 high-risk participants with a false-positive rate of 29% (and 336 low-risk participants with a false-negative rate of 19%); 40% of high-risk participants developed IA within 1 year and 71% within 5 years.

LIMITATIONS

External validation is required. Recruitment occurred over 13 years, with lower rates of IA in later years. There was geographic variation in laboratory testing and recruitment availability.

CONCLUSION

The simple score identified persons at low risk for IA who were less likely to need secondary care. The comprehensive score identified high-risk persons who could benefit from risk stratification and preventive measures. Both scores may be useful in clinical care and should also be useful in clinical trials.

PRIMARY FUNDING SOURCE

National Institute for Health and Care Research Leeds Biomedical Research Centre.

Erratum to: Methods for evaluating medical tests and biomarkers

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

T cell subsets: an immunological biomarker to predict progression to clinical arthritis in ACPA-positive individuals

Objectives

Anticitrullinated protein antibody (ACPA)+ individuals with non-specific musculoskeletal symptoms are at risk of inflammatory arthritis (IA). This study aims to demonstrate the predictive value of T cell subset quantification for progression towards IA and compare it with previously identified clinical predictors of progression.

Methods

103 ACPA+ individuals without clinical synovitis were observed 3-monthly for 12 months and then as clinically indicated. The end point was the development of IA. Naïve, regulatory T cells (Treg) and inflammation related cells (IRCs) were quantified by flow cytometry. Areas under the ROC curve (AUC) were calculated. Adjusted logistic regressions and Cox proportional hazards models for time to progression to IA were constructed.

Results

Compared with healthy controls (age adjusted where appropriate), ACPA+ individuals demonstrated reduced naïve (22.1% of subjects) and Treg (35.8%) frequencies and elevated IRC (29.5%). Of the 103 subjects, 48(46.6%) progressed. Individually, T cell subsets were weakly predictive (AUC between 0.63 and 0.66), although the presence of 2 T cell abnormalities had high specificity. Three models were compared: model-1 used T cell subsets only, model-2 used previously published clinical parameters, model-3 combined clinical data and T cell data. Model-3 performed the best (AUC 0.79 (95% CI 0.70 to 0.89)) compared with model-1 (0.75 (0.65 to 0.86)) and particularly with model-2 (0.62 (0.54 to 0.76)) demonstrating the added value of T cell subsets. Time to progression differed significantly between high-risk, moderate-risk and low-risk groups from model-3 (p=0.001, median 15.4 months, 25.8 months and 63.4 months, respectively).

Conclusions

T cell subset dysregulation in ACPA+ individuals predates the onset of IA, predicts the risk and faster progression to IA, with added value over previously published clinical predictors of progression.

Abnormal T cell differentiation persists in patients with rheumatoid arthritis in clinical remission and predicts relapse

OBJECTIVES

An abnormal CD4+ T cell subset related to inflammation exposure (inflammation-related cells, IRC) has been identified in rheumatoid arthritis (RA). Patients with inflammatory and non-inflammatory diseases were used to examine the relationship between inflammation and this T cell subset in vivo.

METHODS

Blood was collected from healthy controls and patients with RA (active disease or in clinical remission), Crohn's disease and osteoarthritis. IRC and chemokine receptors were quantified by flow cytometry. Thymic activity and apoptotic factors were measured by real-time polymerase chain reaction. Circulating cytokines were measured by enzyme-linked immunosorbent assay. CXCR4 and SDF1 in synovial biopsies were measured using immunohistochemistry.

RESULTS

IRC were identified in patients with RA (p<0.0001) and Crohn's disease (p = 0.005), but not in those with osteoarthritis. In RA in remission, IRC persisted (p<0.001). In remission, hyperproliferation of IRC was lost, chemokine receptor expression was significantly lowered (p<0.007), Bax expression dropped significantly (p<0.001) and was inversely correlated with IRC (rho = -0.755, p = 0.03). High IRC frequency in remission was associated with relapse within 18 months (OR = 6.4, p<0.001) and a regression model predicted 72% of relapse.

CONCLUSIONS

These results suggest a model in which, despite the lack of systemic inflammation, IRC persist in remission, indicating that IRC are an acquired feature of RA. They have, however, lost their hyper-responsiveness, acquired a potential for survival, and no longer express chemokine receptors. IRC persistence in remission confirms their important role in chronic inflammation as circulating precursors of pathogenic cells. This was further demonstrated by much higher incidence of relapse in patients with high IRC frequency in remission.