Added value of multiple autoantibody testing for predicting progression to inflammatory arthritis in at-risk individuals

RMD Open, vol. 8, pp. e002512

Abstract

BACKGROUND: Predicting progression to clinical arthritis in individuals at-risk of developing rheumatoid arthritis is a prerequisite to developing stratification groups for prevention strategies. Selecting accurate predictive criteria is the critical step to define the population at-risk. While positivity for anti-citrullinated protein antibodies (ACPA) remains the main recruitment biomarker, positivity for other autoantibodies (AutoAbs) identified before the onset of symptoms, may provide additional predictive accuracy for stratification. OBJECTIVE: To perform a multiple AutoAbs analysis for both the prediction and the time of progression to inflammatory arthritis (IA). METHODS: 392 individuals were recruited based on a new musculoskeletal complaint and positivity for ACPA or rheumatoid factor (RF). ELISAs were performed for ACPA, RF, anti-nuclear Ab, anti-carbamylated protein (anti-CarP) and anti-collagen AutoAbs. Logistic and COX regression were used for analysis. RESULTS: Progression to IA was observed in 125/392 (32%) of cases, of which 78 progressed within 12 months. The AutoAbs ACPA, RF, anti-CarP were individually associated with progression (p<0.0001) and improved prediction when combined with demographic/clinical data (Accuracy >77%; area under the curve (AUC) >0.789), compared with prediction using only demographic/clinical data (72.9%, AUC=0.760). Multiple AutoAbs testing provided added value, with +6.4% accuracy for number of positive AutoAbs (AUC=0.852); +5.4% accuracy for AutoAbs levels (ACPA/anti-CarP, AUC=0.832); and +6.2% accuracy for risk-groups based on high/low levels (ACPA/RF/anti-CarP, AUC=0.837). Time to imminent progression was best predicted using ACPA/anti-CarP levels (AUC=0.779), while the number of positive AutoAbs was/status/risk were as good (AUC=0.778). CONCLUSION: We confirm added value of multiple AutoAbs testing for identifying progressors to clinical disease, allowing more specific stratification for intervention studies.

Authors 8

1. Frédérique Ponchel corresponding

   University of Leeds

   Affiliation as printed

   Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK

2. Laurence M Duquenne

   University of Leeds

   Affiliation as printed

   Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK

3. Xuanxiao Xie

   University of Leeds

   Affiliation as printed

   Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK

4. Diane Corscadden

   University of Leeds

   Affiliation as printed

   Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK

5. Farag Shuweihdi

   University of Leeds

   Affiliation as printed

   Leeds Institute of Health Sciences, Faculty of Medicine, University of Leeds, Leeds, UK

6. Kulveer Singh Mankia

   University of Leeds

   Affiliation as printed

   Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK

7. Leendert Adrianus Trouw Aachen Department of Immunology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   Department of Immunology, Leiden University Medical Center, Leiden, Netherlands

8. Paul E. Emery

   University of Leeds · Leeds Teaching Hospitals NHS Trust

   Affiliation as printed

   Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, Leeds, UK

   Leeds NIHR Biomedical Research Centre, Leeds Teaching Hospitals Trust, Leeds, UK