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Record 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 on 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

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGiant cell arteritisMedicineGenetic heterogeneityArteritisEvolutionary biologyPathologyGeneticsVasculitisPhenotypeDiseaseBiologyGene

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.086
GPT teacher head0.359
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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