Comprehensive analysis of the major histocompatibility complex in systemic sclerosis identifies differential HLA associations by clinical and serological subtypes

Objective: The greatest genetic effect reported for systemic sclerosis (SSc) lies in the major histocompatibility complex (MHC) locus. Leveraging the largest SSc genome-wide association study, we aimed to fine-map this region to identify novel human leucocyte antigen (HLA) genetic variants associated with SSc susceptibility and its main clinical and serological subtypes. Methods: 9095 patients with SSc and 17 584 controls genome-wide genotyped were used to impute and test single-nucleotide polymorphisms (SNPs) across the MHC, classical HLA alleles and their composite amino acid residues. Additionally, patients were stratified according to their clinical and serological status, namely, limited cutaneous systemic sclerosis (lcSSc), diffuse cutaneous systemic sclerosis (dcSSc), anticentromere (ACA), antitopoisomerase (ATA) and anti-RNApolIII autoantibodies (ARA). Results: Sequential conditional analyses showed nine SNPs, nine classical alleles and seven amino acids that modelled the observed associations with SSc. This confirmed previously reported associations with HLA-DRB1∗11:04 and HLA-DPB1∗13:01, and revealed a novel association of HLA-B∗08:01. Stratified analyses showed specific associations of HLA-DQA1∗02:01 with lcSSc, and an exclusive association of HLA-DQA1∗05:01 with dcSSc. Similarly, private associations were detected in HLA-DRB1∗08:01 and confirmed the previously reported association of HLA-DRB1∗07:01 with ACA-positive patients, as opposed to the HLA-DPA1∗02:01 and HLA-DQB1∗03:01 alleles associated with ATA presentation. Conclusions: This study confirms the contribution of HLA class II and reveals a novel association of HLA class I with SSc, suggesting novel pathways of disease pathogenesis. Furthermore, we describe specific HLA associations with SSc clinical and serological subtypes that could serve as biomarkers of disease severity and progression

High sensitivity and negative predictive value of the DETECT algorithm for an early diagnosis of pulmonary arterial hypertension in systemic sclerosis: application in a single center

Abstract Background Pulmonary arterial hypertension (PAH) is one of the most relevant causes of death in systemic sclerosis. The aims of this study were to analyse the recently published DETECT algorithm comparing it with European Society of Cardiology/European Respiratory Society (ESC/ERS) 2009 guidelines: as screening of PAH; (2) identifying median pulmonary arterial pressure (mPAP) ≥21 mmHg; and (3) determining any group of pulmonary hypertension (PH). Methods Eighty-three patients fulfilling LeRoy’s systemic sclerosis diagnostic criteria with at least right heart catheterization were studied retrospectively. Clinical data, serological biomarkers, echocardiographic and hemodynamic features were collected. SPSS 20.0 was used for statistical analysis. Results According to right heart catheterization findings, 35 patients with PAH and 28 with no PH met the standards for DETECT algorithm analysis: 27.0% of patients presented with functional class III/IV. Applying DETECT, the sensitivity was 100%, specificity 42.9%, the positive predictive value 68.6% and the negative predictive value 100%, whereas employing the ESC/ERS guidelines these were 91.4%, 85.7%, 88.9% and 89.3%, respectively. There were no missed diagnoses of PAH using DETECT compared with three patients missed (8.5%) using ESC/ERS guidelines. The DETECT algorithm also showed greater sensitivity and negative predictive value to identify patients with mPAP ≥21 mmHg or with any type of PH. Conclusions The DETECT algorithm is confirmed as an excellent screening method due to its high sensitivity and negative predictive value, minimizing missed diagnosis of PAH. DETECT would be accurate either for early diagnosis of borderline mPAP or any group of PH