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Background: Giant cell arteritis is an age-related vasculitis that mainly affects the aorta and its branches in individuals aged 50 years and older. Current options for diagnosis and treatment are scarce, highlighting the need to better understand its underlying pathogenesis. Genome-wide association studies (GWAS) have emerged as a powerful tool for unravelling the pathogenic mechanisms involved in complex diseases. We aimed to characterise the genetic basis of giant cell arteritis by performing the largest GWAS of this vasculitis to date and to assess the functional consequences and clinical implications of identified risk loci., Methods: We collected and meta-analysed genomic data from patients with giant cell arteritis and healthy controls of European ancestry from ten cohorts across Europe and North America. Eligible patients required confirmation of giant cell arteritis diagnosis by positive temporal artery biopsy, positive temporal artery doppler ultrasonography, or imaging techniques confirming large-vessel vasculitis. We assessed the functional consequences of loci associated with giant cell arteritis using cell enrichment analysis, fine-mapping, and causal gene prioritisation. We also performed a drug repurposing analysis and developed a polygenic risk score to explore the clinical implications of our findings., Findings: We included a total of 3498 patients with giant cell arteritis and 15 550 controls. We identified three novel loci associated with risk of giant cell arteritis. Two loci, MFGE8 (rs8029053; p=4·96 × 10 -8 ; OR 1·19 [95% CI 1·12-1·26]) and VTN (rs704; p=2·75 × 10 -9 ; OR 0·84 [0·79-0·89]), were related to angiogenesis pathways and the third locus, CCDC25 (rs11782624; p=1·28 × 10 -8 ; OR 1·18 [1·12-1·25]), was related to neutrophil extracellular traps (NETs). We also found an association between this vasculitis and HLA region and PLG. Variants associated with giant cell arteritis seemed to fulfil a specific regulatory role in crucial immune cell types. Furthermore, we identified several drugs that could represent promising candidates for treatment of this disease. The polygenic risk score model was able to identify individuals at increased risk of developing giant cell arteritis (90th percentile OR 2·87 [95% CI 2·15-3·82]; p=1·73 × 10 -13 )., Interpretation: We have found several additional loci associated with giant cell arteritis, highlighting the crucial role of angiogenesis in disease susceptibility. Our study represents a step forward in the translation of genomic findings to clinical practice in giant cell arteritis, proposing new treatments and a method to measure genetic predisposition to this vasculitis., Funding: Institute of Health Carlos III, Spanish Ministry of Science and Innovation, UK Medical Research Council, and National Institute for Health and Care Research., Competing Interests: Declaration of interests MCC reports support from the Spanish Ministry of Science and Innovation (PID2020-114909RB-I00), Vasculitis Foundation, Agency for the Management of University and Research Grants (2021 SGR 01561), and Kiniksa Pharmaceuticals; consulting fees or honoraria from GSK, CSL Vifor, AbbVie, and AstraZeneca; support for attending meetings from Kiniksa Pharmaceuticals; and participation on a data safety monitoring board or advisory board for GSK, CSL Vifor, and AstraZeneca. GE has acted as a consultant for GSK, AstraZeneca, Sobi, Novartis, Boehringer, and CSL Vifor. AWM reports support from the UK Medical Research Council (MRC), National Institute for Health and Care Research (NIHR), Leeds Care, and Roche Products; and consulting fees or honoraria from CSL Vifor and AstraZeneca. PL reports grants or contracts from the Federal Ministry of Education and Research, German Research Society, German Society for Rheumatology, John Grube Foundation, and CSL Vifor; consulting fees or honoraria from GSK, CSL Vifor, AstraZeneca, Bristol Myers Squibb, Boehringer Ingelheim, Forum für medizinische Fortbildung, Janssen, Rheumaakademie, and UCB; support for attending meetings from CSL Vifor; and participation on a data safety monitoring board or advisory board for GSK, CSL Vifor, AbbVie, and Novartis. TW reports consulting fees or honoraria from AbbVie, AstraZeneca, Lilly, UCB, and Novartis; and participation on a data safety monitoring board or advisory board for AbbVie, AstraZeneca, Lilly, UCB, Novartis, and Fresenius. MAG-G reports honoraria from GSK. NK reports grants or contracts from Bristol Myers Squibb, AbbVie, and Sanofi; and consulting fees or honoraria from Roche, Otsuka, GSK, and Mallinckrodt. CAL reports grants or contracts from Bristol Myers Squibb and support from the National Institutes of Health. PAM reports grants, contracts, or consulting fees from AbbVie, Amgen, AstraZeneca, ArGenx, Boehringer Ingelheim, Bristol Myers Squibb, Cabaletta, CSL Behring, Eicos, Electra, Forbius, Genentech–Roche, GSK, HiBio, InflaRx, Janssen, Jubilant, Kyverna, MiroBio, Neutrolis, Novartis, NS Pharma, Q32, Regeneron, Sanofi, Sparrow, Takeda, and Vistera; royalties or licenses from UpToDate; and stock or stock options from Kyverna, Q32, and Sparrow. SLM reports grants or contracts from MRC, NIHR, and CSL Vifor; consulting fees from Roche, Sanofi, AbbVie, AstraZeneca, and Pfizer; payment or honoraria for lectures or educational events from Roche, Pfizer, UCB, CSL Vifor, Fresenius Kabi, and Novartis; support for attending meetings from Pfizer; participation on a data safety monitoring board or advisory board for Collaboration for Leadership in Applied Health Research and Care, Haywood Foundation, and GC-SheaLD; a leadership or fiduciary role in the British Society for Rheumatology Clinical Affairs Committee; participation as an investigator on industry-sponsored clinical trials for Sanofi; and infrastructure support from MRC. LB reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Instrumentation Laboratory SPA and AbbVie; and support for attending meetings from AbbVie and Novartis. EB reports payments or honoraria from EULAR and received grants from the Dutch Arthritis Society DAS and the EU/EFPIA/Innovative Medicines Initiative 2 Joint Undertaking Immune-Image grant no 831514. EB is member of the board of the non-profit organisation, Auto-immune Research Hub, in the Netherlands. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Objectives: Efficacy evaluation of GCA treatment is primarily based on non-specific symptoms and laboratory markers. We aimed to assess the change in vascular inflammation in patients with large vessel (LV)-GCA under different treatments using [18F]FDG PET/CT., Methods: Observational study on patients with new-onset, active LV-GCA starting treatment with either prednisolone monotherapy (PRED) or combination with MTX or tocilizumab (TOC). All patients underwent baseline and follow-up PET/CT. The aorta and its major branches were assessed using PET vascular activity score (PETVAS) by independent readers. Cumulative glucocorticoid doses and cessation of glucocorticoid treatment were documented in all patients., Results: We included 88 LV-GCA patients, 27 were treated with PRED, 42 with MTX and 19 with TOC. PETVAS decreased from 18.9-8.0 units at follow-up in the overall population (P <0.001). PETVAS changes were numerically higher in patients receiving MTX (-12.3 units) or TOC (-11.7 units) compared with PRED (-8.7). Mean cumulative prednisolone dosages were 5637, 4418 and 2984 mg in patients treated with PRED, MTX and TOC (P =0.002). Risk ratios for glucocorticoid discontinuation at the time of follow-up PET/CT were 6.77 (95% CI: 1.01, 45.29; P =0.049) and 16.25 (95% CI: 2.60, 101.73; P =0.003) for MTX and TOC users compared with PRED users., Conclusion: Treatment of LV-GCA inhibits vascular inflammation in the aorta and its major branches. While similar control of vascular inflammation was achieved with PRED, MTX and TOC treatments, TOC showed a strong glucocorticoid sparing effect, supporting the concept of initial combination therapy., (© The Author(s) 2021. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Giant cell arteritis (GCA) is the most common form of vasculitis in individuals older than 50 years in Western countries. To shed light onto the genetic background influencing susceptibility for GCA, we performed a genome-wide association screening in a well-powered study cohort. After imputation, 1,844,133 genetic variants were analyzed in 2,134 case subjects and 9,125 unaffected individuals from ten independent populations of European ancestry. Our data confirmed HLA class II as the strongest associated region (independent signals: rs9268905, p = 1.94 × 10 -54 , per-allele OR = 1.79; and rs9275592, p = 1.14 × 10 -40 , OR = 2.08). Additionally, PLG and P4HA2 were identified as GCA risk genes at the genome-wide level of significance (rs4252134, p = 1.23 × 10 -10 , OR = 1.28; and rs128738, p = 4.60 × 10 -9 , OR = 1.32, respectively). Interestingly, we observed that the association peaks overlapped with different regulatory elements related to cell types and tissues involved in the pathophysiology of GCA. PLG and P4HA2 are involved in vascular remodelling and angiogenesis, suggesting a high relevance of these processes for the pathogenic mechanisms underlying this type of vasculitis., (Copyright © 2017 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

We conducted a large-scale genetic analysis on giant cell arteritis (GCA), a polygenic immune-mediated vasculitis. A case-control cohort, comprising 1,651 case subjects with GCA and 15,306 unrelated control subjects from six different countries of European ancestry, was genotyped by the Immunochip array. We also imputed HLA data with a previously validated imputation method to perform a more comprehensive analysis of this genomic region. The strongest association signals were observed in the HLA region, with rs477515 representing the highest peak (p = 4.05 × 10(-40), OR = 1.73). A multivariate model including class II amino acids of HLA-DRβ1 and HLA-DQα1 and one class I amino acid of HLA-B explained most of the HLA association with GCA, consistent with previously reported associations of classical HLA alleles like HLA-DRB1(∗)04. An omnibus test on polymorphic amino acid positions highlighted DRβ1 13 (p = 4.08 × 10(-43)) and HLA-DQα1 47 (p = 4.02 × 10(-46)), 56, and 76 (both p = 1.84 × 10(-45)) as relevant positions for disease susceptibility. Outside the HLA region, the most significant loci included PTPN22 (rs2476601, p = 1.73 × 10(-6), OR = 1.38), LRRC32 (rs10160518, p = 4.39 × 10(-6), OR = 1.20), and REL (rs115674477, p = 1.10 × 10(-5), OR = 1.63). Our study provides evidence of a strong contribution of HLA class I and II molecules to susceptibility to GCA. In the non-HLA region, we confirmed a key role for the functional PTPN22 rs2476601 variant and proposed other putative risk loci for GCA involved in Th1, Th17, and Treg cell function., (Copyright © 2015 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)