| 1. |
- Khatri, B., et al.
(author)
-
Genome-wide association study identifies Sjogren's risk loci with functional implications in immune and glandular cells
- 2022
-
In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 13:1
-
Journal article (peer-reviewed)abstract
- Sjogren's disease is a complex autoimmune disease with twelve established susceptibility loci. This genome-wide association study (GWAS) identifies ten novel genome-wide significant (GWS) regions in Sjogren's cases of European ancestry: CD247, NAB1, PTTG1-MIR146A, PRDM1-ATG5, TNFAIP3, XKR6, MAPT-CRHR1, RPTOR-CHMP6-BAIAP6, TYK2, SYNGR1. Polygenic risk scores yield predictability (AUROC = 0.71) and relative risk of 12.08. Interrogation of bioinformatics databases refine the associations, define local regulatory networks of GWS SNPs from the 95% credible set, and expand the implicated gene list to >40. Many GWS SNPs are eQTLs for genes within topologically associated domains in immune cells and/or eQTLs in the main target tissue, salivary glands. The genetic architecture underlying Sjogren's syndrome is not fully understood. Here, the authors perform a genome-wide association study to identify 10 new genetic risk regions, implicating genes involved in immune and salivary gland function.
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
|
|
| 7. |
- Retamozo, S., et al.
(author)
-
Influence of the age at diagnosis in the disease expression of primary Sjogren's syndrome : Analysis of 12,753 patients from the Sjogren Big Data Consortium
- 2021
-
In: Clinical and Experimental Rheumatology. - : CLINICAL & EXPER RHEUMATOLOGY. - 0392-856X .- 1593-098X. ; 39:6, s. S166-S174
-
Journal article (peer-reviewed)abstract
- Objective. To analyse how the main components of the disease phenotype (sicca symptoms, diagnostic tests, immunological markers and systemic disease) can be driven by the age at diagnosis of primary Sjogren's syndrome (pSS).Methods. By January 2021, the participant centres had included 12,753 patients from 25 countries that fulfilled the 2002/2016 classification criteria for pSS. The age at diagnosis was defined as the time when the attending physician confirmed fulfilment of the criteria. Patients were clustered according to age at diagnosis. 50 clusters with more than 100 observations (from 27 to 76 years) were used to study the influence of the age at diagnosis in the disease expression.Results. There was a consistent increase in the frequency of oral dryness according to the age at diagnosis, with a frequency of <90% in patients diagnosed at the youngest ages and >95% in those diagnosed at the oldest ages. The smooth curves that best fitted a linear model were the frequency of dry mouth (adjusted R-2 0.87) and the frequency of abnormal oral tests (adjusted R-2 0.72). Therefore, for each 1-year increase in the age at diagnosis, the frequency of dry mouth increased by 0.13%, and the frequency of abnormal oral diagnostic tests by 0.11%. There was a consistent year-by-year decrease in the frequency of all autoantibodies and immunological markers except for cryoglobulins. According to the linear models, for each 1-year increase in the age at diagnosis, the frequency of a positive result decreased by 0.57% (for anti-Ro antibodies), 0.47% (for RF) and 0.42% (for anti-La antibodies). The ESSDAI domains which showed a more consistent decrease were glandular and lymph node involvement (for each 1-year increase in the age at diagnosis, the frequency of activity decreased by 0.18%), and constitutional, cutaneous, and haematological involvements (the frequency decreased by 0.09% for each 1-year increase). In contrast, other domains showed an ascending pattern, especially pulmonary involvement (for each 1-year increase in the age at diagnosis, the frequency of activity increased by 0.22%), and peripheral nerve involvement (the frequency increased by 0.09% for each 1-year increase).Conclusion. The influence of the age at diagnosis on the key phenotypic features of pSS is strong, and should be considered critical not only for designing a personalised diagnostic approach, but also to be carefully considered when analysing the results of diagnostic tests and immunological parameters, and when internal organ involvement is suspected at diagnosis.
|
|
| 8. |
- Ternant, D, et al.
(author)
-
TROUGH CONCENTRATION AND ESTIMATED CLEARANCE CAN DETECT IMMUNOGENICITY TO ADALIMUMAB IN RA PATIENTS: A PROSPECTIVE LONGITUDINAL MULTICENTRE STUDY
- 2020
-
In: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 79, s. 1449-1450
-
Conference paper (other academic/artistic)abstract
- Anti-Drug Antibodies (ADA) to adalimumab increase drug clearance in rheumatoid arthritis (RA).Objectives:To study the ability of drug concentration or estimating clearance to identify ADA to adalimumab.Methods:Adalimumab concentration was measured with a validated ELISA. ADA was measured using a capture ELISA (Theradiag®) and the Meso scale discovery (MSD) platform. Using a bayesian PK model, adalimumab clearance was estimated at 1, 3, 6 and 12 months. Predictions for ADA presence were calculated, and the correlation between ADA and adalimumab clearance was analysed.Results:We analyzed 108 samples from 53 RA patients. Serum concentrations and clearance estimates showed good prediction performance for ADA presence (Table 1). There was a correlation between adalimumab clearance and ADA (Figure 1).Table 1.Immunogenicity prediction of adalimumab, using trough concentration or estimated clearanceTime of visitADA methodAdalimumab trough concentrationAdalimumab estimated clearanceAUC ROCp-valueAUC ROCp-valueMonth 1THER.55.6411.52.8358MSD.65.0821.61.1872Month 3THER.89.0006.91.0003MSD.73.0096.72.0131Month 6THER.95.0035.95.0035MSD.85.0004.84.0006Month 12THER.87.0045.86.0057MSD.88.0002.88.0002Figure 1.correlation between adalimumab estimated clearance and ADA as provided by the Meso scale discovery (MSD) plateformConclusion:Adalimumab concentration and clearance should be considered as reliable predictors for ADA presence in RA patients.Acknowledgments:Measurement of adalimumab serum concentrations was performed within the ‘Centre pilote de suivi biologique des anticorps thérapeutiques’ (CePiBAc)– Pilot centre for therapeutic antibodies monitoring platform of Tours University Hospital, which was cofinanced by the European Regional Development Fund (ERDF). We thank Oscar Knight, Delphine Delord and Fabien Giannoni (ABIRISK lab technician), Caroline Brochon and Anne Claire Duveau (CePIBAc), Aliette Decock-Giraudaud (Centre de ressource-Biobank), Sophie Tourdot (ABRISIK Project manager), Aline Doublet (Assistance Publique Hopitaux de Paris, Agnès Hincelin-Méry (Sanofi, Chilly-Mazarin, France). This work has received support from the Innovative Medicines Initiative Joint Undertaking (IMI JU) under grant agreement no. 115303, the resources of which are composed of financial contributions from the European Union’s Seventh Framework Programme (FP7/2007-2013) and European Federation of Pharmaceutical Industries and Associations (EFPIA) companies’ in-kind contributions.Disclosure of Interests:David Ternant Consultant of: Sanofi and Amgen., Jamal Elhasnaoui: None declared, Natacha Szely: None declared, Salima Hacein-Bey: None declared, Aude Gleizes: None declared, Christophe Richez Consultant of: Abbvie, Amgen, Mylan, Pfizer, Sandoz and UCB., Jessica Manson: None declared, Martin SOUBRIER: None declared, Olilvier Brocq: None declared, Jérôme Avouac: None declared, Anna Fogdell-Hahn Grant/research support from: Biogen Idec and Pfizer., Consultant of: Pfizer, Biogen, Merck-Serono, and Sanofi-Genzyme., Pierre Dönnes: None declared, Gilles Paintaud Grant/research support from: Amgen, Genzyme (Sanofi), Lilly, Merck, Novartis, and Roche Pharma., Consultant of: Chugai, Novartis and Shire (Takeda), with remunerations received by his institution., Céline Desvignes: None declared, Florian Deisenhammer: None declared, Sebastian Spindeldreher Employee of: Novartis, Marc Pallardy: None declared, Xavier Mariette Consultant of: BMS, Gilead, Medimmune, Novartis, Pfizer, Servier, UCB, Denis Mulleman Grant/research support from: Non-governmental organisation Lions Club Tours Val de France, French Society for Rheumatology., Consultant of: Pfizer, Novartis.
|
|
| 9. |
|
|
| 10. |
- Ishigaki, Kazuyoshi, et al.
(author)
-
Multi-ancestry genome-wide association analyses identify novel genetic mechanisms in rheumatoid arthritis
- 2022
-
In: Nature Genetics. - : Springer Nature. - 1061-4036 .- 1546-1718. ; 54:11, s. 1640-1651
-
Journal article (peer-reviewed)abstract
- Rheumatoid arthritis (RA) is a highly heritable complex disease with unknown etiology. Multi-ancestry genetic research of RA promises to improve power to detect genetic signals, fine-mapping resolution and performances of polygenic risk scores (PRS). Here, we present a large-scale genome-wide association study (GWAS) of RA, which includes 276,020 samples from five ancestral groups. We conducted a multi-ancestry meta-analysis and identified 124 loci (P < 5 × 10−8), of which 34 are novel. Candidate genes at the novel loci suggest essential roles of the immune system (for example, TNIP2 and TNFRSF11A) and joint tissues (for example, WISP1) in RA etiology. Multi-ancestry fine-mapping identified putatively causal variants with biological insights (for example, LEF1). Moreover, PRS based on multi-ancestry GWAS outperformed PRS based on single-ancestry GWAS and had comparable performance between populations of European and East Asian ancestries. Our study provides several insights into the etiology of RA and improves the genetic predictability of RA.
|
|
