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Serum aminoacyl-tRNA synthetase-interacting multifunctional protein-1 (AIMP1), a novel disease activity predictive biomarker of systemic lupus erythematosus
S.S. Ahn1, S.H. Hong2, Y. Park3, S.M. Jung4, J.J. Song5, Y.-B. Park6, S.-W. Lee7, S.G. Park8
- Division of Rheumatology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.
- College of Pharmacy, Ajou University, Suwon, Gyunggido, South Korea.
- Department of Laboratory Medicine, Yonsei University College of Medicine, Seoul, South Korea.
- Division of Rheumatology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.
- Division of Rheumatology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.
- Division of Rheumatology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea.
- Division of Rheumatology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul, South Korea. sangwonlee@yuhs.ac
- College of Pharmacy, Ajou University, Suwon, Gyunggido, South Korea. sgpark@ajou.ac.kr
CER10668
2018 Vol.36, N°4
PI 0533, PF 0539
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PMID: 29352840 [PubMed]
Received: 06/07/2017
Accepted : 17/10/2017
In Press: 15/01/2018
Published: 19/07/2018
Abstract
OBJECTIVES:
Secreted aminoacyl-tRNA synthetase-interacting multifunctional protein-1 (AIMP1) has been reported to have pro-inflammatory properties. The aim of this study was to evaluate the clinical significance of serum AIMP1 in patients with systemic lupus erythematosus (SLE).
METHODS:
Serum levels of AIMP1 were measured in 160 patients with SLE using a human AIMP1 ELISA kit. Eighty patients were classified as active SLE (SLEDAI-2K ≥ 5), and 80 patients were classified as stable SLE. Correlation between serum AIMP1, SLE disease activity index-2000 (SLEDAI-2K), and laboratory variables related to disease activity or inflammatory burdens were assessed using Pearson’s correlation analysis. The optimal cut-off value for serum AIMP1 to predict active SLE was estimated by using a receiver operator characteristic curve, and logistic regression analysis was used to compare the odds ratios (ORs) of laboratory variables in predicting active SLE.
RESULTS:
The median serum AIMP1 was higher in patients with active SLE than those with stable SLE (8.0 vs. 6.5 ng/ml, p<0.001). Serum AIMP1 demonstrated correlation with SLEDAI-2K and laboratory variables related to disease activity or inflammatory burdens. The optimal cut-off AIMP1 to predict active SLE was 10.09. Multivariate logistic regression analysis including conventional laboratory variables demonstrated that serum AIMP1 ≥10.09 ng/ml (OR 3.919, 95% confidence interval 1.223−12.564, p=0.022) was useful in predicting active SLE.
CONCLUSIONS:
Serum levels of AIMP1 were associated with disease activity of SLE and could predict active SLE based on SLEDAI-2K.