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Record W4411433090 · doi:10.1016/j.ard.2025.05.193

OP0180 ACUTE PSORIATIC DACTYLITIS: THE ROLE OF HLA GENETIC SUSCEPTIBILITY MARKERS AND CLINICAL ASSOCIATIONS

2025· article· en· W4411433090 on OpenAlexaff
Fadi Kharouf, V. Carrizo Abarza, Pankti Mehta, Sheng Gao, D. Ganatra, D. Periera, Rachel J. Cook, D. Poddubnyy, Dafna Gladman, V. Chandran

Bibliographic record

VenueAnnals of the Rheumatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicRheumatoid Arthritis Research and Therapies
Canadian institutionsUniversity of WaterlooKrembil Foundation
Fundersnot available
KeywordsMedicineDactylitisHuman leukocyte antigenPsoriasisPsoriatic arthritisImmunologyDermatologyGenetic predispositionEnthesitisPathologyAntigenDisease

Abstract

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Background: The Human Leukocyte Antigen (HLA) region, particularly the HLA-B and HLA-C genes, plays a crucial role in susceptibility to and the clinical phenotype of Psoriatic Arthritis (PsA). Objectives: This study aimed to investigate the HLAs associated with the occurrence and course of acute/tender dactylitis in PsA patients. Methods: We analyzed data from a prospective observational cohort of PsA patients. We identified visits with acute dactylitis and screened each HLA-B or HLA-C allele with a prevalence >5% in our database for an association with this outcome by employing univariable logistic regression, fitted using generalized estimating equations (GEE). We selected alleles with a p-value ≤0.2 for further analysis, including HLA-B*08, HLA-B*15, HLA-B*27, HLA-B*40, HLA-B*51, HLA-C*02, HLA-C*03, and HLA-C*12 . We then performed a multivariable logistic regression, fitted using GEE and adjusted for the calendar decade (1978-1988 used as reference, 1989-1999, 2000-2010, 2011-2024), to examine factors associated with the occurrence of acute dactylitis. We used Cox regression models, adjusted for the calendar decade, to explore factors influencing the time to resolution of the first event of acute dactylitis. Lastly, we performed sensitivity analyses incorporating clinically relevant haplotypes ( HLA-B*8/C*07, HLA-B*27/C*01, HLA-B*27/C*02, HLA-B*38/C*12, HLA-B*44/C*05, HLA-B*44/C*16 and HLA-B*57/C*06 ) instead of alleles. Results: Among the 1216 patients included, 612 (52%) experienced at least one event of acute dactylitis over a median follow-up duration of 12.0 [IQR: 5.7–20.1] years (Table 1). Six hundred and ninety-nine (57.5%) patients were male, with a mean age at baseline (clinic entry) of 44.0 (SD 12.9) years. The proportion of patients with HLA-B*27 was 16.6%, HLA-B* 38/C* 12 was 10.9%, and 8.6% were HLA-B* 27/C*02 positive. In the multivariable GEE analysis (Table 2), adjusted for the calendar decade, we found that the presence of HLA-B*27 (OR 1.71, 95% CI 1.22–2.39) and HLA-C* 12 (OR 1.43, 95% CI 1.05–1.95) alleles was positively associated with the occurrence of acute dactylitis. In contrast, HLA-B*51 (OR 0.60, 95% CI 0.37–0.95) showed a negative association. Other clinical factors independently associated with the occurrence of acute dactylitis included younger age in years (OR 0.97, 95% CI 0.96–0.98), male sex (OR 1.61, 95% CI 1.28–2.03), shorter duration of PsA (OR 0.98, 95% CI 0.96–0.99), higher body mass index (OR 1.02, 95% CI 1.01–1.04), and the use of non-steroidal anti-inflammatory drugs (OR 1.65, 95% CI 1.32–2.05). Conversely, having been on biologic or targeted synthetic disease-modifying anti-rheumatic drugs (DMARDs) at the time of the acute dactylitis (OR 0.38, 95% CI 0.29–0.49) showed a negative association. In the multivariable Cox regression model, adjusted for the calendar decade, we found that none of the HLA alleles was independently associated with the time to resolution of acute dactylitis. Initiating biologic or targeted synthetic DMARDs was associated with faster resolution of acute dactylitis (OR 1.67, 95% CI 1.17–2.36). In sensitivity analyses incorporating haplotypes instead of alleles, the haplotypes HLA-B* 38/C* 12 (OR 1.51, 95% CI 1.14–2.01) and HLA-B* 27/C* 02 (OR 1.69, 95% CI 1.17–2.44) were independently associated with the occurrence of acute dactylitis. None of the haplotypes showed a significant association with the time to resolution of acute dactylitis. Conclusion: HLA-B*27 and HLA-C*12 alleles, as well as the haplotypes HLA-B*38/C* 12 and HLA-B*27/C* 02, are positively associated with the occurrence of acute PsA dactylitis. In contrast, HLA-B*51 shows a negative association. None of these HLAs are associated with the course of acute dactylitis, investigated as the time to resolution. These findings may contribute to a deeper understanding of the interplay between genetic factors and clinical outcomes in PsA. REFERENCES: NIL . Table 1Patient characteristics at the time of clinic entry.VariableOverall(n=1216)Never had dactylitis(n=632)Ever had dactylitis(n=584)Age in years, mean (SD)44.0 (12.9)46.0 (13.2)41.9 (12.3)Sex (male), n (%)699 (57.5)332 (52.5)367 (62.8)EthnicityWhite, n (%)1033 (85.3)520 (82.5)513 (88.3)Duration of PsA in years, median [IQR]2.9 [0.8, 8.4]2.8 [0.7, 8.9]2.9 [0.8, 8.3]BMI in kg/m2, mean (SD)28.8 (6.2)28.73 (6.45)28.8 (5.98)Acute dactylitis, n (%)338 (27.9)-338 (58.0)PASI (0-72), median [IQR]1.8 [0.0, 4.6]1.8 [0.0, 4.9]1.4 [0.0, 4.4]DAPSA, median [IQR]16.8 [9.0, 29.0]16.6 [9.8, 28.7]17.0 [9.0, 29.0]Modified Steinbrocker score, median [IQR]2.0 [0.0, 9.0]0.0 [0.0, 6.0]2.0 [0.0, 12.0]Sacroiliitis, n (%)246 (23.2)117 (22.1)129 (24.3)CRP in mg/dL, mean (SD)13.8 (20.7)13.2 (20.2)14.8 (21.6)HLA-B*27, n (%)201 (16.6)76 (12.1)125 (21.4)HLA-B*51, n (%)70 (5.8)38 (6.0)32 (5.5)HLA-C*12, n (%)253 (20.9)121 (19.2)132 (22.8)HLA-B*27/C*02, n (%)105 (8.6)35 (5.5)70 (12.0)HLA-B*38/C*12, n (%)133 (10.9)58 (9.2)75 (12.8)NSAIDs, n (%)819 (67.4)385 (60.9)434 (74.3)csDMARDs, n (%)467 (38.4)232 (36.7)235 (40.2)Biologic or tsDMARDs, n (%)105 (8.6)78 (12.3)27 (4.6)SD, standard deviation; PsA, psoriatic arthritis; IQR, interquartile range; BMI, body mass index; PASI, Psoriasis Area and Severity Index; DAPSA, Disease Activity Index for Psoriatic Arthritis; CRP, C-reactive protein; HLA, Human Leukocyte Antigen; NSAIDs: Non-steroidal anti-Inflammatory drug; cs, conventional synthetic; DMARDs: disease-modifying anti-rheumatic drugs; ts, targeted synthetic Acknowledgements: NIL . Disclosure of Interests: Fadi Kharouf: None declared, Virginia Carrizo Abarza: None declared, Pankti Mehta: None declared, Shangyi Gao: None declared, Darshini Ganatra: None declared, Daniel Periera: None declared, Richard Cook: None declared, Denis Poddubnyy AbbVie, Canon, DKSH, Eli Lilly, Janssen, MSD, Medscape, Novartis, Peervoice, Pfizer, and UCB, AbbVie, Biocad, Bristol-Myers Squibb, Eli Lilly, Janssen, Moonlake, Novartis, Pfizer, and UCB, AbbVie, Eli Lilly, Janssen, Novartis, Pfizer, UCB, Dafna D. Gladman AstraZeneca, Abbvie, Amgen, BMS, Eli Lilly, GSK, Janssen, Novartis, Pfizer, UCB, Abbvie, Amgen, Eli Lilly, Janssen, Novartis, Pfizzer, UCB, Vinod Chandran AbbVie, BMS, Eli Lilly, Fresenius Kabi, Johnson and Johnson, Novartis, UCB, AbbVie, Eli Lilly. © 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.

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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)0.002
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.356
Teacher spread0.335 · 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
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