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Objectives Emerging evidence demonstrates that aPS-PT associate with thrombotic events. Genetic predisposition, including HLA-DRB1 alleles, is known to contribute to the occurrence of conventional aPL [anti-β2glycoprotein-I (anti-β2GPI) and aCL]. We investigated associations between aPS-PT and HLA-DRB1 * alleles and thrombosis in SLE. Conventional aPL were included for comparison. Methods We included 341 consecutive SLE patients, with information on general cardiovascular risk factors, including blood lipids, LA and thrombotic events. aPS/PT, anti-β2GPI and aCL of IgA/G/M isotypes and LA were quantified. Results aPS/PT antibodies associated positively with HLA-DRB1 *13 [odds ratio (OR) 2.7, P  = 0.002], whereas anti-β2GPI and aCL antibodies associated primarily with HLA-DRB1*04 (OR 2.5, P  = 0.0005). These associations remained after adjustment for age, gender and other HLA-DRB1 * alleles. HLA-DRB1*13 , but not DRB1*04 , remained as an independent risk factor for thrombosis and APS after adjustment for aPL and cardiovascular risk factors. The association between DRB1*13 and thrombosis was mediated by aPS-PT positivity. HLA-DRB1*03 , on the other hand, associated negatively with thrombotic events as well as all aPL using both uni- and multivariate analyses. HLA-DRB1*03 had a thrombo-protective effect in aPL-positive patients. Additionally, HLA-DRB1*03 was associated with a favourable lipid profile regarding high-density lipoprotein and triglycerides. Conclusions HLA-DRB1*13 confers risk for both aPS-PT and thrombotic events in lupus. The association between HLA-DRB1*13 and thrombosis is largely, but not totally, mediated through aPS-PT. HLA-DRB1*03 was negatively associated with aPL and positively with favourable lipid levels. Thus, HLA-DRB1*03 seems to identify a subgroup of SLE patients with reduced vascular risk. [ABSTRACT FROM AUTHOR]

Objectives This study aimed to characterize the systemic lipid profile of patients with asymptomatic hyperuricemia (HUA) and gout using lipidomics, and to find potential underlying pathological mechanisms therefrom. Methods Sera were collected from Affiliated Hospital of Nanjing University of Chinese Medicine as centre 1 (discovery and internal validation sets) and Suzhou Hospital of Traditional Chinese Medicine as centre 2 (external validation set), including 88 normal subjects, 157 HUA and 183 gout patients. Lipidomics was performed by ultra high performance liquid chromatography plus Q-Exactive mass spectrometry (UHPLC-Q Exactive MS). Differential metabolites were identifed by both variable importance in the projection ≥1 in orthogonal partial least-squares discriminant analysis mode and false discovery rate adjusted P ≤ 0.05. Biomarkers were found by logistic regression and receiver operating characteristic (ROC) analysis. Results In the discovery set, a total of 245 and 150 metabolites, respectively, were found for normal subjects vs HUA and normal subjects vs gout. The disturbed metabolites included diacylglycerol, triacylglycerol (TAG), phosphatidylcholine, phosphatidylethanolamine, phosphatidylinositol, etc. We also found 116 differential metabolites for HUA vs gout. Among them, the biomarker panel of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 could differentiate well between HUA and gout. The area under the receiver operating characteristic ROC curve was 0.8288, the sensitivity was 82% and the specificity was 78%, at a 95% CI 0.747, 0.9106. In the internal validation set, the predictive accuracy of TAG 18:1-20:0-22:1 and TAG 14:0-16:0-16:1 panel for differentiation of HUA and gout reached 74.38%, while it was 84.03% in external validation set. Conclusion We identified serum biomarkers panel that have the potential to predict and diagnose HUA and gout patients. [ABSTRACT FROM AUTHOR]