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Abstract Number: 490

Clinical and Biomarker Factors in the Prediction of Future Inflammatory Arthritis in ACPA Positive Subjects without Inflammatory Arthritis at Baseline

John P. Gerstenberger1, Colin I. O'Donnell2, Sarah L. Dill3, Randall Tagg4, Masoud Asadi-Zeydabadi4, M. Kristen Demoruelle5, V. Michael Holers6 and Kevin D. Deane7, 1Department of Medicine, University of Colorado Denver School of Medicine, Aurora, CO, 2University of Colorado Denver School of Medicine, Aurora, CO, 3Division of Rheumatology, University of Colorado Denver School of Medicine, Aurora, CO, 4Department of Physics, University of Colorado Denver, Denver, CO, 51775 Aurora Ct, 1775 Aurora Ct, Aurora, CO, 6Rheumatology Division, University of Colorado School of Medicine, Aurora, CO, 7Division of Rheumatology, University of Colorado School of Medicine, Aurora, CO

Meeting: 2017 ACR/ARHP Annual Meeting

Date of first publication: September 18, 2017

Keywords: Rheumatoid arthritis (RA)

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Session Information

Date: Sunday, November 5, 2017

Title: Rheumatoid Arthritis – Human Etiology and Pathogenesis Poster I

Session Type: ACR Poster Session A

Session Time: 9:00AM-11:00AM

Background/Purpose:

Antibodies to citrullinated protein antigens (ACPA) can identify individuals who may develop future inflammatory arthritis (IA) and classifiable rheumatoid arthritis (RA). Indeed, there are clinical trials underway where ACPA(+) individuals are treated to determine how IA/RA can be prevented or delayed. However, not all ACPA(+) subjects progress to IA/RA within the 2-3 years that is often duration for a clinical prevention trial. As such, identifying ways to predict which ACPA(+) subjects will most likely progress to IA/RA within defined time periods could improve selection of individuals for RA prevention trials as well as help to improve understanding of the biology of IA/RA development.

 

Methods:

21 ACPA(+) (CCP3, Inova) individuals without historical or current IA were identified at a Colorado-based health-fair. These individuals were followed prospectively for up to 24 months for incident IA/RA. Clinical and examination factors, and biomarkers including the shared epitope, CCP3, RF-IgM (Inova), and high sensitivity C-reactive protein (hsCRP), were evaluated to determine which factors predicted incident IA/RA. Specifically, optimal levels for association with IA/RA of autoantibodies and hsCRP were determined with machine learning techniques, and these optimal levels were then evaluated with other factors to identify the overall best models for discrimination of the development of IA/RA.

 

Results:

9 of 21 subjects (43%) developed IA/RA after a median of 11 months (Table 1). In univariate analyses of baseline factors, only self-reported joint stiffness was significantly associated with the development of IA/RA. Results from AUC analyses incorporating various variables are presented in Table 2. Overall, models using a combination of demographic and clinic factors, and optimized biomarker levels, had the highest discrimination (AUC 1.0) for the development of future IA/RA.

 

Conclusion:

Individuals at high risk for progression to IA/RA can be identified through health-fair evaluations for ACPA. Furthermore, clinically-obtainable factors including biomarkers that are optimized using machine learning techniques can be used to determine with high discrimination which serum ACPA(+) individuals will develop IA/RA within 24 months. Caveats include that this is a small sample set with potential overfitting of the models in regard to the optimized levels of biomarkers; therefore, these findings need additional validation. Nevertheless, this approach appears useful to better identify candidates for IA/RA preventive interventions.

 

Table 1. Descriptions and univariate analyses of baseline variables of subjects identified with anti-CCP3 positivity in absence of IA

 

Incident IA/RA

N=9

No IA/RA

N=12

P-value

Months to IA/RA or total follow-up, median (range)

11 (4-23)

10 (6-24)

1.00

Age, median (range)

55 (40-73)

48 (29-83)

0.31

Female, N (%)

4 (44%)

6 (50%)

1.00

Non-Hispanic White, N (%)

8 (89%)

9 (75%)

0.60

Ever Smoker, N (%)

6 (67%)

5 (42%)

0.39

Current Smoker, N (%)

0 (0%)

1 (8%)

1.00

Pack-Years, median (range)

4 (0-18)

0 (0-18)

0.35

Weekly alcohol intake >=1 unit, N (%)

8 (89%)

10 (83%)

1.00

Self-report FDR, N (%)

2 (22%)

1 (8%)

0.55

Shared Epitope Positive, N (%)

5 (56%)

3 (25%)

0.20

CCP3 Positive, N (%) (>=20 units)

9 (100%)

12 (100%)

1.00

CCP3 Level, median (range)

78 (27-262)

55 (24-377)

0.60

RF-IgM Positive, N (%) (>=13.6 units)

3 (33%)

3 (25%)

1.00

RF-IgM Level, median (range)

7 (1-120)

2 (0-44)

0.55

hsCRP positive, N (%)(>10 mg/L)

0 (0%)

0 (0%)

1.00

hsCRP Level, median (range)

1.4 (0.2-5.3)

0.8 (0.3-5.1)

0.38

Self-Report Joint Pain (Any Joint of 68), N (%)

6 (67%)

3 (25%)

0.09

Self-Report Morning Joint Stiffness (Any duration, any joint of 68)*

6 (67%)

2 (17%)

0.03

Subjects with >=1 tender joint on 68 joint examination, N (%)

3 (33%)

0 (0%

0.06

# of Tender Joints on 68 joint examination, median (range)

0 (0-1)

0 (0-0)

1.00

Subjects with >=1 swollen joint consistent with RA-like synovitis on 66 joint examination

0 (0%)

0 (0%)

1.00

Meeting 2010 ACR/EULAR Criteria at time of development of inflammatory arthritis, N (%)

7 (78%)

0 (0%)

–

*No subject reported a duration of morning joint stiffness of >60 minutes; for all subjects, the overall median (range) of duration of joint stiffness was 20 minutes (0-60).

 

Table 2. Discrimination of future IA/RA

Variables in model

AUC

Core variables* plus CCP3, RF-IgM and hsCRP at optimized cut-off levels***

1.0

Core variables* plus CCP3 and RF-IgM at standard cut-off levels

0.90

Core variables* alone

0.76

*Core variables include age, gender, race, history of smoking (ever/never), presence of shared epitope (present/absent), self-reported joint pain (present/absent), self-reported joint stiffness (present/absent), and joint tenderness on examination (present/absent).

**Standard biomarker cut-off levels for positivity are: CCP3 >=20 units; RF-IgM >13.6 units; hsCRP >10 mg/L.

***Optimized biomarker levels are: CCP3 >42.61 units; RF-IgM 0.69 units; hsCRP 0.85 mg/L.

 


Disclosure: J. P. Gerstenberger, None; C. I. O'Donnell, None; S. L. Dill, None; R. Tagg, None; M. Asadi-Zeydabadi, None; M. K. Demoruelle, None; V. M. Holers, None; K. D. Deane, Inova Diagnostics, Inc., 5.

To cite this abstract in AMA style:

Gerstenberger JP, O'Donnell CI, Dill SL, Tagg R, Asadi-Zeydabadi M, Demoruelle MK, Holers VM, Deane KD. Clinical and Biomarker Factors in the Prediction of Future Inflammatory Arthritis in ACPA Positive Subjects without Inflammatory Arthritis at Baseline [abstract]. Arthritis Rheumatol. 2017; 69 (suppl 10). https://acrabstracts.org/abstract/clinical-and-biomarker-factors-in-the-prediction-of-future-inflammatory-arthritis-in-acpa-positive-subjects-without-inflammatory-arthritis-at-baseline/. Accessed .
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