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THU0013 Towards the definition of a risk model profile of pericarditis in systemic lupus erythematosus: a genetic study
  1. C Perricone1,
  2. F Ceccarelli1,
  3. C Ciccacci2,
  4. S Rufini2,
  5. C Alessandri1,
  6. FR Spinelli1,
  7. E Cipriano1,
  8. C Politi2,
  9. A Latini2,
  10. G Novelli2,
  11. G Valesini2,
  12. P Borgiani2,
  13. F Conti1
  1. 1Lupus Clinic, Rheumatology, Department of Internal Medicine, Sapienza University of Rome
  2. 2Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy


Background We have recently demonstrated that polymorphisms (SNPs) in TRAF3IP2 gene are associated with susceptibility for systemic lupus erythematosus (SLE) and can predispose to the development of pericarditis (1). Moreover, we found that other genes previously associated with susceptibility to SLE (MIR1279, STAT4, PTPN2) seem to be involved in the development of pericarditis (2). At this purpose, we built a genetic risk model for the development of pericarditis in SLE (3).

Objectives We aimed to expand the knowledge on the genetic risk of pericarditis in SLE by studying the role of rs2205960 (TNFSF4) and rs2233945 (ATG16L1) SNPs, previously associated with SLE susceptibility, improving our genetic risk model.

Methods We recruited SLE patients (diagnosed according to 1997 revised ACR criteria) and healthy subjects served as controls. Study protocol included complete physical examination and blood drawing. The clinical and laboratory data were collected in a standardized, computerized, electronically-filled form (3). Clinical and laboratory features were assessed with a dichotomous score (present=1; absent=0). SNPs genotyping was performed by allelic discrimination assay. A case/control association study and a genotype/phenotype correlation analysis were performed and a risk profile model for pericarditis in SLE was built.

Results Three-hundred fifteen SLE patients [285 F (90.5%), 30 M (9.5%), mean age 43.11±11.28 years, mean age at onset 32.19±11.84 years] and 278 healthy controls were enrolled. Pericarditis was present in 56 (17.8%) SLE patients. Deviations from Hardy–Weinberg equilibrium for the studied SNPs were not observed. The variant alleles of the rs2205960 (TNFSF4, P=0.013, OR=2.14) and of the rs2233945 (ATG16L1 P=0.009, OR=2.32) were significantly associated with susceptibility to pericarditis. A risk profile model for pericarditis considering the risk alleles of TRAF3IP2, MIR1279, STAT4, PTPN2, TNFSF4 and ATG16L1 showed that patients with more than 5 risk alleles have a significantly higher risk to develop pericarditis (P<0.001, OR=8.01). Anti-Sm antibodies were the only laboratory parameter associated with the development of pericarditis. Thus, a multivariate analysis by binary regression analysis, considering as dependent variable the presence or absence of pericarditis and as independent variables all the studied SNPs associated with pericarditis, was performed. In a stepwise approach including anti-Sm, TRAF3IP2, MIR1279, STAT4, PTPN2, TNFSF4 and ATG16L1 SNPs, the model explains about 25% (R2 Cox &Snell) of the variability involved in the susceptibility to pericarditis.

Conclusions We describe for the first time the contribution of TNFSF4 and ATG16L1 SNPs in pericarditis development in SLE patients. We improved our genetic risk profile model to better and earlier identify SLE patients more susceptible to develop this complication.


  1. Perricone C, Ciccacci C, et al. Immunogenetics. 2013 Oct;65(10):703–9.

  2. Ciccacci C, Perricone C, et al. PLoS One. 2014 Nov 4;9(11):e111991.

  3. Ciccacci C, Perricone C, et al. Lupus. 2016. doi: 10.1177/0961203316679528.


Disclosure of Interest None declared

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