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Record W4409839207 · doi:10.1016/j.autrev.2025.103824

The emerging concept of ANCA-associated vasculitis related to inborn errors of immunity

2025· review· en· W4409839207 on OpenAlexafffund
Clément Triaille, Benjamin Terrier, Alice Hadchouel, Élie Haddad, Augusto Vaglio, Marie‐Louise Frémond

Bibliographic record

VenueAutoimmunity Reviews · 2025
Typereview
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineUniversité de Montréal
FundersCHU Sainte-Justine FoundationWallonie-Bruxelles International
KeywordsANCA-Associated VasculitisVasculitisMedicineImmunologyImmunityPathologyImmune systemDisease

Abstract

fetched live from OpenAlex

ANCA-associated vasculitis (AAV) is a group of rare small vessels vasculitis that preferentially affect the kidneys, lungs and upper airways. Although the detailed pathophysiology remains unclear, genetic background has been shown to play a role in sporadic forms of AAV. The discovery of these susceptibility genes (and associated biological pathways) involved in AAV have shaped the current understanding of AAV pathophysiology. In addition to common genetic polymorphisms, specific rare inborn errors of immunity (IEI) have been described with a high frequency of ANCA (antineutrophil cytoplasmic antibodies) positivity and vasculitis features in young individuals (in addition to other manifestations). A systematic literature search revealed that patients with pathogenic variants in COPA, STING1, DNASE1L3, and PIK3CD are at increased risk of developing ANCA and AAV features, including alveolar hemorrhage, interstitial lung disease, pauciimmune glomerulonephritis, and upper airways involvement (septum perforation, saddle-nose deformity, chronic nasal/sinuses ulceration). Some of these IEI may also present with a mixed phenotype and/or auto-antibodies profile associating features of AAV and other autoimmune diseases (in particular systemic lupus erythematosus). Notably, a proportion of reports and series lack serological (ANCA specificity and titers) and/or histopathological data, making challenging to assess the likelihood for ANCA pathogenicity in some patients with IEI (as opposed to unspecific signs of biologic autoimmunity). This point is nonetheless essential to make appropriate therapeutic decisions. In addition, since most of the genes mentioned above are involved in the type 1 interferon signaling, the role of this pathway in AAV etiopathogenesis deserves further investigation. In this review, we will describe these IEI, their overlap with sporadic AAV, and their evocative features. Next, we will discuss how these monogenic conditions might inform our general understanding of AAV pathophysiology. We also propose some directions for future research in order to better define the link between ANCA and IEI. Finally, we will consider how making the diagnosis of an IEI in a patient with AAV features might impact individual management. Schematic clinical approach to the diagnosis and management of inborn errors of immunity with a presentation of ANCA-associated vasculitis (created with biorender.com ). Abbreviations: AAV: ANCA-associated vasculitis; ANCA: Antineutrophil cytoplasmic antibodies; COPA: Coatomer protein complex subunit α; DNASE1l3: Deoxyribonuclease 1 L3; IAH: Intra-alveolar hemorrhage; IFN: Interferon; ILD: Interstitial lung disease; JIA: Juvenile idiopathic arthritis; MPO: Myeloperoxidase; PIK3CD: Phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic Subunit Delta; PR3: Proteinase 3; SAVI: STING-associated vasculopathy with onset in infancy; SLE: Systemic lupus erythematosus • Increased risk of AAV has been identified in specific rare inborn errors of immunity (IEI). • The identification of monogenic forms of AAV has opened new research avenues to study AAV. • Sporadic and monogenic forms of AAV display both similarities and differences; some clues should prompt testing for IEI. • Diagnosing an IEI in a patient with AAV-like phenotype will impact the individual management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.354
Teacher spread0.318 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations7
Published2025
Admission routes2
Has abstractyes

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