Deciphering Rheumatoid Arthritis-associated Interstitial Lung Disease Phenotypes Using Unsupervised Hierarchical Clustering Analysis: Results from a Large Collaborative International Study

Pierre-Antoine Juge¹, Benjamin Granger², Lina El Houari³, Gregory McDermott⁴, Tracy J. Doyle⁴, Clive Kelly⁵, Gouri M Koduri⁶, Robert Vassallo⁷, Amarilys Alarcon Calderon⁷, Umut Kalyoncu⁸, Antonio Gonzalez⁹, Natalia Mena Vazquez¹⁰, Marta Rojas¹¹, Raquel Dos-Santos¹², Miriam Retuerto-Guerrero¹³, Cristina Vadillo-Font¹⁴, PALOMA VELA¹⁵, Antonio Fernandez-Nebro¹⁶, Alejandro Escudero-Contreras¹⁷, Eva Perez-Pampin¹⁸, Charlotte Hyldgaard¹⁹, Antoine Froidure²⁰, Patrick Durez²¹, Jorge Rojas Serrano²², Coline van Moorsel²³, Jan C Grutters²³, Leticia Kawano-Dourado²⁴, Charlotte Lucas²⁵, Stephane Jouneau²⁶, Raimon Sanmarti²⁷, Raul Castellanos Moreira²⁸, Katarina Antoniou²⁹, Maria Molina molina³⁰, joshua Solomon³¹, Sophia raia³¹, Miguel A Gonzalez-Gay³², Belén Atienza-Mateo³³, Sofia Flouda³⁴, Effrosyni Manali³⁴, Boumpas Dimitrios³⁵, Spyros Papiris³⁴, THEOFANIS KARAGEORGAS³⁶, Marco Sebastiani³⁷, Andreina Manfredi³⁸, Ana Catarina Duarte³⁹, Santos Castañeda⁴⁰, Lorenzo Cavagna⁴¹, Bruno Crestani⁴², Jeffrey Sparks⁴³ and Philippe Dieudé⁴⁴, ¹Division of Rheumatology, Inflammation, and Immunity Brigham & Women's Hospital, Boston, MA, ²Sorbonne Université, INSERM, and Pitié Salpêtrière Hospital, Paris, France, ³Institut Pierre Louis d’Epidémiologie et de Santé Publique, Paris, France, ⁴Brigham and Women's Hospital, Boston, MA, ⁵James Cook University Hospital, Middlesbrough, United Kingdom, ⁶Southend University Hospitals NHS Trust, Essex, United Kingdom, ⁷Mayo Clinic, Rochester, MN, ⁸Hacettepe University, Faculty of Medicine, Department of Internal Medicine, Division of Rheumatology, Ankara, Turkey, ⁹Hospital Clinico Universitario de Santiago, Santiago de Compostela, Spain, ¹⁰IBIMA, Málaga, Spain, ¹¹Hospital Universitario Reina Sofía, Cordoba, Spain, ¹²Clinical University Hospital in Santiago de Compostela, Santiago de Compostela, Spain, ¹³Rheumatology, Hospital Universitario de León, León, Spain, ¹⁴Hospital Clínico San Carlos – Instituto Investigación Sanitaria San Carlos (IdISSC), Madrid, Spain, ¹⁵Rheumatology, Hospital General Universitario Alicante, Alicante, Spain, ¹⁶Hospital Regional Universitario de Málaga, Malaga, Spain, ¹⁷Rheumatology Department, Reina Sofia University Hospital, Cordoba/Maimonides Biomedical Research Institute of Cordoba (IMIBIC)/University of Cordoba, Córdoba, Spain, ¹⁸Rheumatology Department Complejo Hospitalario Universitario Santiago, Santiago de compostela, Spain, ¹⁹Silkeborg Regional Hospital, Silkeborg, Denmark, ²⁰Université Catholique de Louvain, Louvain, Belgium, ²¹UCLouvain, Brussels, Belgium, ²²Hospital Angeles Villahermosa, Ciudad de Mexico, Mexico, ²³St. Antonius Hospital, Nieuwegein, Netherlands, ²⁴Hospital do Coração (HCor), São Paulo, Brazil, ²⁵CHU, Rennes, France, ²⁶Amicus, Boca Raton, FL, ²⁷Hospital Clinic, Rheumatology, Barcelona, Spain, ²⁸Hospital Clínic de Barcelona, Madrid, Spain, ²⁹University of Crete, Heraklion, Greece, ³⁰IDIBELL Bellvitge Biomedical Research Institute, Barcelona, Spain, ³¹National Jewish Health, Denver, CO, ³²IDIVAL and School of Medicine, UC, Santander; Department of Rheumatology, IIS-Fundación Jiménez Díaz, Madrid, Santander, Spain, ³³Rheumatology, Hospital Universitario Marqués de Valdecilla, IDIVAL, Santander, Spain, ³⁴Unit of Attikon University Hospital, Athens, Greece, ³⁵National and Kapodistrian University of Athens, Athens, Greece, ³⁶n/a, Athina, Greece, ³⁷Azienda Policlinico di Modena, Modena, Italy, ³⁸University of Modena, Modena, Italy, ³⁹Hospital Garcia de Orta, Almada, Portugal, ⁴⁰Hospital Universitario de la Princesa, Madrid, Spain, ⁴¹Fondazione IRCCS Policlinico San Matteo, Pavia, Italy, ⁴²Hopital Bichat, Paris University, Paris, France, ⁴³Division of Rheumatology, Inflammation, and Immunity, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, ⁴⁴Assistance Publique-Hôpitaux de Paris, Bichat-Claude Bernard University Hospital, INSERM UMR1152, University de Paris Cité, Department of Rheumatology, Paris, France

Meeting: ACR Convergence 2023

Keywords: interstitial lung disease, rheumatoid arthritis

Session Information

Date: Sunday, November 12, 2023

Title: Abstracts: RA – Diagnosis, Manifestations, & Outcomes I: RA-ILD

Session Type: Abstract Session

Session Time: 2:00PM-3:30PM

Background/Purpose: RA-associated interstitial lung disease (ILD) is not a single entity as illustrated by different HRCT patterns, different risk factors and prognoses suggesting heterogenous phenotypes. Our objective was to identify definite sub-phenotypes among a large RA-ILD dataset performing unsupervised clustering analysis.

Methods: In this international collaborative study (22 centers from 13 countries), we included patients with RA (ACR/EULAR 2010 classification criteria) having ILD defined by high-resolution computed tomography (HRCT) chest scan. Clinical, biological, imaging, and functional data were retrospectively collected from extensive chart review. Missing data were handled using multiple imputation with chained equations. A multiple correspondence analysis was performed on each sample to reduce dimensions and a non-parametric Bayesian algorithm was used to attribute a cluster to each patient. The clusters were aggregated over the multiple imputations using weighted Non-negative Matrix Factorization.MUC5B rs35705950 T risk allele distribution in the identified clusters was investigate for patients with available genotype. A surrogate decision tree was conducted using the non-imputed dataset to predict a cluster for each patient. The prediction error rate of the tree was estimated using cross-validation.

Results: A total of 1696 patients with RA-ILD were included in the cluster analysis; Table 1. Among the 37 collected variables, 20 active variables were used to construct clusters. The non-parametric Bayesian clustering algorithm detected 2 definite clusters: cluster 1 (n=1015) and cluster 2 (n=681); Table1, Figure 1. Compared to cluster 2, patients from cluster 1 were more frequently male (45.5% vs 38.3%, p=0.003), older at RA onset (56.9 ± 13.3 vs 53.8 ± 14.5, p< 0.001) and at ILD onset (65.5 ± 10.1 vs 61.9 ± 12.3, p< 0.001), more frequently with European ethnicity (85.9% vs 68.3%, p< 0.001), more frequently ever smokers at ILD onset (64.2% vs 55.7%, p< 0.001) and had less Sjogren syndrome (10.8% vs 20.6%, p< 0.001). They were more frequently positive for anti-citrullinated peptides antibodies (90.7% vs 70.0%, p< 0.001) and for rheumatoid factor (95.8% vs 67.4%, p< 0.001). UIP HRCT pattern was predominant in cluster 1 (72.5%) whereas various patterns were observed in cluster 2 (UIP = 30.8%, NSIP = 17.2%), p< 0.001. There was no difference in lung function at ILD onset between the 2 clusters. MUC5B rs35705950 genotype was available for 444 patient (26.2%) with a higher minor allele frequency (MAF) in cluster 1 (33% compared to 18%, p< 0.001). Figure 2 shows the decision tree that can be used to predict the cluster for a new patient with a prediction error rate of 9.8%.

Conclusion: Our results provide evidence that RA-ILD is a heterogeneous disease and allow the identification of at least two distinct subsets. This heterogeneity is illustrated by a contribution of the MUC5B promoter variant restricted to cluster 1, promoting the identification of specific risk scores for each RA-ILD subset. This heterogeneity underlies distinct physio pathological mechanisms which could influence not only the prognosis of patients with RA-ILD but also their therapeutic management.

Table 1. Distribution of active variables within the clusters among patients with RA-ILD (n=1696). Data are numbers (percentages) for categorical variables or means (Standard Deviation) for continuous variables. 95% confidence interval for clustering analysis are indicated in the brackets for each variable. Normalized ACPA and RF represents fold above upper limit normal.
ACPA: anti-citrullinated peptides antibodies; DLCO: diffusion capacity of the lung for carbon monoxide; HRCT: high-resolution computed tomography; ILD: interstitial lung disease; LIP: lymphocytic interstitial pneumonia; NSIP: non-specific interstitial pneumonia; OP: organizing pneumonia; RF: rheumatoid factor; UIP: usual interstitial pneumonia

Figure 1. Clusters representation on the first three dimensions of Multiple Correspondence Analysis (MCA)
The map is a 3D representation of patients and their clusters (n = 1696). A point represents a patient, and its color represents the cluster assigned to this patient (cluster 1 in blue and cluster 2 in orange). The first three dimensions of MCA together explain 27.7% of the total variance.

Figure 2. Decision tree to predict a cluster for a new patient with RA-ILD
The tree is a set of paths. Each path is a sequence of conditions that lead to a decision (cluster 1 or cluster 2). The nodes present active variables that are important in the decision-making and the edges present conditions to be checked by a patient in order to classify him in one of the two clusters. The barplot (final node of each path) presents the two clusters (cluster 1 and cluster 2) on the left and the probability to be in cluster 1 on the right. ACPA: anti-citrullinated peptides antibodies; HRCT: high-resolution computed tomography; ILD: interstitial lung disease; LIP: lymphocytic interstitial pneumonia; NSIP: non-specific interstitial pneumonia; OP: organizing pneumonia; RF: rheumatoid factor; UIP: usual interstitial pneumonia

Disclosures: P. Juge: None; B. Granger: Bristol-Myers Squibb(BMS), 2; L. El Houari: None; G. McDermott: None; T. Doyle: None; C. Kelly: None; G. Koduri: None; R. Vassallo: None; A. Alarcon Calderon: None; U. Kalyoncu: None; A. Gonzalez: None; N. Mena Vazquez: None; M. Rojas: None; R. Dos-Santos: None; M. Retuerto-Guerrero: None; C. Vadillo-Font: None; P. VELA: AbbVie/Abbott, 5, AstraZeneca, 5, Eli Lilly, 5, 6, GlaxoSmithKlein(GSK), 6, Novartis, 5, Pfizer, 5; A. Fernandez-Nebro: None; A. Escudero-Contreras: None; E. Perez-Pampin: None; C. Hyldgaard: None; A. Froidure: None; P. Durez: None; J. Rojas Serrano: None; C. van Moorsel: None; J. Grutters: None; L. Kawano-Dourado: None; C. Lucas: None; S. Jouneau: None; R. Sanmarti: None; R. Castellanos Moreira: None; K. Antoniou: None; M. Molina molina: None; j. Solomon: None; S. raia: None; M. Gonzalez-Gay: AbbVie/Abbott, 5, 6, Amgen, 5, 6, Pfizer, 5, 6; B. Atienza-Mateo: None; S. Flouda: None; E. Manali: None; B. Dimitrios: None; S. Papiris: None; T. KARAGEORGAS: None; M. Sebastiani: None; A. Manfredi: None; A. Duarte: None; S. Castañeda: None; L. Cavagna: None; B. Crestani: None; J. Sparks: AbbVie, 2, Amgen, 2, Boehringer Ingelheim, 2, Bristol-Myers Squibb, 2, 5, Gilead, 2, Inova Diagnostics, 2, Janssen, 2, Optum, 2, Pfizer, 2, ReCor, 2; P. Dieudé: AbbVie, 2, 6, Boehringer Ingelheim, 1, 2, 6, Bristol-Myers Squibb, 1, 2, 5, 6, Chugai, 5, Galapagos, 5, 6, Janssen, 2, 6, Novartis, 2, Pfizer, 1, 2, 5, 6.

To cite this abstract in AMA style:

Juge P, Granger B, El Houari L, McDermott G, Doyle T, Kelly C, Koduri G, Vassallo R, Alarcon Calderon A, Kalyoncu U, Gonzalez A, Mena Vazquez N, Rojas M, Dos-Santos R, Retuerto-Guerrero M, Vadillo-Font C, VELA P, Fernandez-Nebro A, Escudero-Contreras A, Perez-Pampin E, Hyldgaard C, Froidure A, Durez P, Rojas Serrano J, van Moorsel C, Grutters J, Kawano-Dourado L, Lucas C, Jouneau S, Sanmarti R, Castellanos Moreira R, Antoniou K, Molina molina M, Solomon j, raia S, Gonzalez-Gay M, Atienza-Mateo B, Flouda S, Manali E, Dimitrios B, Papiris S, KARAGEORGAS T, Sebastiani M, Manfredi A, Duarte A, Castañeda S, Cavagna L, Crestani B, Sparks J, Dieudé P. Deciphering Rheumatoid Arthritis-associated Interstitial Lung Disease Phenotypes Using Unsupervised Hierarchical Clustering Analysis: Results from a Large Collaborative International Study [abstract]. Arthritis Rheumatol. 2023; 75 (suppl 9). https://acrabstracts.org/abstract/deciphering-rheumatoid-arthritis-associated-interstitial-lung-disease-phenotypes-using-unsupervised-hierarchical-clustering-analysis-results-from-a-large-collaborative-international-study/. Accessed .

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