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Enregistrement W4411421342 · doi:10.1016/j.ard.2025.05.479

POS0082 EXPLORING THE GENETIC BASIS OF CLINICAL HETEROGENEITY IN GIANT CELL ARTERITIS

2025· article· en· W4411421342 sur OpenAlexaff
Gonzalo Borrego‐Yaniz, V. Fuentes-Moreno, José Hernández‐Rodríguez, Anna Vaglio, S. Castañeda, Roser Solans‐Laqué, Nader Khalidi, C. Langford, Steven R. Ytterberg, Lorenzo Beretta, Marcello Govoni, Giacomo Emmi, Marco A. Cimmino, T Witte, T. Neumann, Julia U. Holle, Verena Schönau, G. Pugnet, NA Papo, J. Haroche, Alfred Mahr, L. Mouthon, Øyvind Molberg, Andreas P. Diamantopoulos, Alexandre E. Voskuyl, Thomas Daikeler, Christoph Berger, Eleanor J. Molloy, D. Blockmans, Yannick van Sleen, S. GCA Group, Norberto Ortego‐Centeno, Elisabeth Brouwer, Peter Lamprecht, Sebastian Klapa, Carlo Salvarani, P. A. Merkel, M.C. Cid, Miguel Á. González‐Gay, Ann W Morgan, Joanna Martin, Ana Márquez

Notice bibliographique

RevueAnnals of the Rheumatic Diseases · 2025
Typearticle
Langueen
DomaineMedicine
ThématiqueVasculitis and related conditions
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésGiant cell arteritisMedicineGenetic heterogeneityArteritisEvolutionary biologyPathologyGeneticsVasculitisPhenotypeDiseaseBiologyGene

Résumé

récupéré en direct d'OpenAlex

Background: Giant cell arteritis (GCA) is a large-vessel vasculitis primarily affecting the aorta and its branches. GCA presents with a wide range of clinical manifestations, reflecting its complex and heterogeneous nature. These disease features include cranial symptoms such as headache, vision abnormalities, and ischemic stroke, as well as the association with polymyalgia rheumatica. This clinical variability complicates diagnosis and management of GCA. Objectives: The aim of this study was to identify genetic risk factors associated with key clinical manifestations of GCA by testing genome-wide association. Methods: Genomic data from 3,498 patients with GCA and 15,550 healthy controls from a previous study [1] were analyzed to investigate the genetic background associated with GCA-related manifestations. Patients with GCA were stratified according to the presence/absence of clinical phenotypes (shown in Table 1 along with their respective sample sizes). Three logistic regression analyses were performed for each trait, adjusting for the first 10 principal components and sex as covariates, comparing: i) manifestation-positive patients and unaffected controls, ii) manifestation-negative patients and unaffected controls, and iii) patients with and without the considered manifestation. A signal was considered specifically associated with a clinical phenotype if it reached genome-wide significance (p < 5×10⁻⁸) in the case-control comparison and also nominal significance in intra-case comparisons. The HLA region was excluded from the analysis due to its high linkage disequilibrium and complex genetic architecture. Gene annotation was conducted based on SNP-to-gene distance and functional information, including expression and protein quantitative trait locus (eQTL/pQTL) data from blood and vascular tissues. Results: Thirteen non-HLA significant genetic associations across seven distinct GCA-related manifestations were identified: early disease onset, polymyalgia rheumatica, visual manifestations, severe ischemic manifestations, jaw claudication, arm or leg claudication, and irreversible occlusive disease (Figure 1). No significant association was found for permanent visual loss. These loci exhibited notable effect sizes and comprised novel associations for this pathology. Key findings included: RHOQ , associated with visual manifestations (rs6746695, p=4.14×10⁻⁸, OR=3.19), which regulates angiogenesis via Notch signaling [2]; IL22RA1 , associated with jaw claudication (rs72663289, p=1.36×10⁻8, OR=3.13), an interleukin receptor that has been described to be upregulated in GCA-affected arteries, as well as peripheral blood mononuclear cells and plasma from patients with GCA [3]; and OTUD1 , associated with the absence of polymyalgia rheumatica (rs138303599, OR=6.10, p=1.01×10-10), which can promote inflammation and remodeling in cardiac tissue by affecting STAT3 [4, 5]. Additionally, the gene P4HA2 , previously identified as involved in GCA, was found to be specifically associated with late-onset cases (rs419291, p=2.89×10⁻⁸, OR=1.19). Conclusion: This study represents the first genome-wide association analysis of GCA-specific manifestations, deepening our understanding of the genetic basis underlying the disease's clinical heterogeneity. These findings may lead to earlier diagnosis, improved monitoring of disease activity, and more targeted therapeutic strategies for this complex condition. REFERENCES: [1] Borrego-Yaniz G, Ortiz-Fernández L, Madrid-Paredes A, et al (2024) Risk loci involved in giant cell arteritis susceptibility: a genome-wide association study. Lancet Rheumatol 6:e374–e383. [2] Bridges E, Sheldon H, Kleibeuker E, et al (2020) RHOQ is induced by DLL4 and regulates angiogenesis by determining the intracellular route of the Notch intracellular domain. Angiogenesis 23:493–513. [3] Zerbini A, Muratore F, Boiardi L, et al (2018) Increased expression of interleukin-22 in patients with giant cell arteritis. Rheumatology (Oxford) 57:64–72. [4] Wang M, Han X, Yu T, et al (2023) OTUD1 promotes pathological cardiac remodeling and heart failure by targeting STAT3 in cardiomyocytes. Theranostics 13:2263–2280. [5] Oikawa D, Gi M, Kosako H, et al (2022) OTUD1 deubiquitinase regulates NF-κB- and KEAP1-mediated inflammatory responses and reactive oxygen species-associated cell death pathways. Cell Death Dis 13:694. Acknowledgements: NIL . Disclosure of Interests: None declared . © The Authors 2025. This abstract is an open access article published in Annals of Rheumatic Diseases under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Neither EULAR nor the publisher make any representation as to the accuracy of the content. The authors are solely responsible for the content in their abstract including accuracy of the facts, statements, results, conclusion, citing resources etc.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,002
Études des sciences et des technologies0,0000,001
Communication savante0,0010,000
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,086
Tête enseignante GPT0,359
Écart entre enseignants0,274 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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Même revueAnnals of the Rheumatic DiseasesMême sujetVasculitis and related conditionsTravaux en français237 207