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Autoantibodies in pediatric systemic lupus erythematosus: Cluster analysis and its clinical implications in Indian children
Siva Vyasam1, Anu Punnen1, Visalakshi Jeyaseelan2, John Jude Prakash3, Sathish Kumar1
1 Department of Pediatrics, Christian Medical College, Vellore, Tamil Nadu, India
2 Department of Biostatistics, Christian Medical College, Vellore, Tamil Nadu, India
3 Department of Microbiology, Christian Medical College, Vellore, Tamil Nadu, India
Correspondence Address:
Sathish Kumar,
Department of Pediatrics, Christian Medical College, Vellore, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None DOI: 10.4103/injr.injr_129_21
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Introduction: In children with Systemic Lupus Erythematosus (SLE), autoantibodies are considered as biomarkers for specific organ involvement or tissue damage. Some autoantibodies are used for diagnosis, disease activity, and few for disease characterisation. By using cluster analysis, we identified antibody clustering in children with SLE with specific subsets of clinical manifestations at the time of diagnosis.
Materials and Methods: All pediatric SLE (pSLE) who fulfilled 4/11 ACR criteria were included in this study. Their autoantibodies profiles were noted. We recruited 212 patients with newly diagnosed pSLE and cluster analysis was done. We identified 3 clusters which were used for analysis.
Results: Cluster 1 had ANA and anti-dsDNA antibodies a low prevalence of all other antibodies. Children in cluster 2 had autoantibodies such as ANA, anti-dsDNA, anti-RNP, and anti-Sm. Cluster 3 was had autoantibodies such as dsDNA, ANA, anti-cardiolipin and anti-SSA antibodies. On analysis, there was statistically significant difference among the 3 clusters for hair loss (P = 0.006), oral ulcers (P = 0.024), arthritis (P = 0.025), neurological symptoms (P = 0.037), renal manifestations (P = 0.003), AIHA (P = 0.012).
Conclusion: In pSLE, autoantibodies clusters with distinct clinical phenotypes. Hence, all autoantibodies should be done at time of diagnosis. It will help in predicting the clinical course of pSLE and also to identify patients at risk of developing major organ involvement later.
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