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- Choi, MY, et al.
(author)
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The prevalence and determinants of anti-DFS70 autoantibodies in an international inception cohort of systemic lupus erythematosus patients
- 2017
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In: Lupus. - : SAGE Publications. - 1477-0962 .- 0961-2033. ; 26:10, s. 1051-1059
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Journal article (peer-reviewed)abstract
- Autoantibodies to dense fine speckles 70 (DFS70) are purported to rule out the diagnosis of SLE when they occur in the absence of other SLE-related autoantibodies. This study is the first to report the prevalence of anti-DFS70 in an early, multinational inception SLE cohort and examine demographic, clinical, and autoantibody associations. Patients were enrolled in the Systemic Lupus International Collaborating Clinics (SLICC) inception cohort within 15 months of diagnosis. The association between anti-DFS70 and multiple parameters in 1137 patients was assessed using univariate and multivariate logistic regression. The frequency of anti-DFS70 was 7.1% (95% CI: 5.7–8.8%), while only 1.1% (95% CI: 0.6–1.9%) were monospecific for anti-DFS70. In multivariate analysis, patients with musculoskeletal activity (Odds Ratio (OR) 1.24 [95% CI: 1.10, 1.41]) or with anti-β2 glycoprotein 1 (OR 2.17 [95% CI: 1.22, 3.87]) were more likely and patients with anti-dsDNA (OR 0.53 [95% CI: 0.31, 0.92]) or anti-SSB/La (OR 0.25 [95% CI: 0.08, 0.81]) were less likely to have anti-DFS70. In this study, the prevalence of anti-DFS70 was higher than the range previously published for adult SLE (7.1 versus 0–2.8%) and was associated with musculoskeletal activity and anti-β2 glycoprotein 1 autoantibodies. However, ‘monospecific’ anti-DFS70 autoantibodies were rare (1.1%) and therefore may be helpful to discriminate between ANA-positive healthy individuals and SLE.
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- Hui-Yuen, JS, et al.
(author)
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Safety and Efficacy of Belimumab to Treat Systemic Lupus Erythematosus in Academic Clinical Practices
- 2015
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In: The Journal of rheumatology. - : The Journal of Rheumatology. - 0315-162X .- 1499-2752. ; 42:12, s. 2288-2295
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Journal article (peer-reviewed)abstract
- To evaluate the use and efficacy of belimumab in academic practices. Belimumab is a human monoclonal antibody that inhibits soluble B lymphocyte stimulator and has been approved for the treatment of adults with systemic lupus erythematosus (SLE).Methods.Invitations to participate and complete a 1-page questionnaire for each patient prescribed belimumab were sent to 16 physicians experienced in SLE phase III clinical trials. The outcome was defined as the physician’s impression of improvement in the initial manifestation(s) being treated without worsening in other organ systems.Results.Of 195 patients treated with belimumab at 10 academic centers, 96% were taking background medications for SLE at initiation of belimumab, with 74% taking corticosteroids. The main indications for initiation of belimumab were arthritis, rash, and/or worsening serologic activity, with 30% of patients unable to taper corticosteroids. Of the 120 patients taking belimumab for at least 6 months, 51% responded clinically and 67% had ≥ 25% improvement in laboratory values. While numbers are limited, black patients showed improvement at 6 months. In a subset of 39 patients with childhood-onset SLE, 65% responded favorably at 6 months, and 35% discontinued corticosteroids.Conclusion.Our data demonstrate favorable clinical and laboratory outcomes in patients with SLE at 6 months across all racial and ethnic groups, with similar improvement seen among patients with childhood-onset SLE.
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- Orbai, A-M, et al.
(author)
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Anti-C1q antibodies in systemic lupus erythematosus.
- 2015
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In: Lupus. - : SAGE Publications. - 0961-2033 .- 1477-0962. ; 24:1, s. 42-49
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Journal article (peer-reviewed)abstract
- Anti-C1q has been associated with systemic lupus erythematosus (SLE) and lupus nephritis in previous studies. We studied anti-C1q specificity for SLE (vs rheumatic disease controls) and the association with SLE manifestations in an international multicenter study.
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- Ravelli, A, et al.
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2016 Classification Criteria for Macrophage Activation Syndrome Complicating Systemic Juvenile Idiopathic Arthritis: A European League Against Rheumatism/American College of Rheumatology/Paediatric Rheumatology International Trials Organisation Collaborative Initiative
- 2016
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In: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 75:3, s. 481-489
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Journal article (peer-reviewed)abstract
- To develop criteria for the classification of macrophage activation syndrome (MAS) in patients with systemic juvenile idiopathic arthritis (JIA). A multistep process, based on a combination of expert consensus and analysis of real patient data, was conducted. A panel of 28 experts was first asked to classify 428 patient profiles as having or not having MAS, based on clinical and laboratory features at the time of disease onset. The 428 profiles comprised 161 patients with systemic JIA—associated MAS and 267 patients with a condition that could potentially be confused with MAS (active systemic JIA without evidence of MAS, or systemic infection). Next, the ability of candidate criteria to classify individual patients as having MAS or not having MAS was assessed by evaluating the agreement between the classification yielded using the criteria and the consensus classification of the experts. The final criteria were selected in a consensus conference. Experts achieved consensus on the classification of 391 of the 428 patient profiles (91.4%). A total of 982 candidate criteria were tested statistically. The 37 best-performing criteria and 8 criteria obtained from the literature were evaluated at the consensus conference. During the conference, 82% consensus among experts was reached on the final MAS classification criteria. In validation analyses, these criteria had a sensitivity of 0.73 and a specificity of 0.99. Agreement between the classification (MAS or not MAS) obtained using the criteria and the original diagnosis made by the treating physician was high (κ=0.76). We have developed a set of classification criteria for MAS complicating systemic JIA and provided preliminary evidence of its validity. Use of these criteria will potentially improve understanding of MAS in systemic JIA and enhance efforts to discover effective therapies, by ensuring appropriate patient enrollment in studies.
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- Romagnoni, A, et al.
(author)
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Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data
- 2019
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In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 9:1, s. 10351-
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Journal article (peer-reviewed)abstract
- Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers.
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