Background: COPA syndrome is an autosomal dominant, severe, rare, monogenic autoimmune disorder, often affecting the lungs and joints, caused by loss-of-function mutations of COPA which are located in a specific locus (i.e., the COPA mutation hotspot, 7th to 10th exons). It is associated with autoimmune features and upregulation of both the type-1 interferon (IFN) pathway and T helper-17 cells. Clinical manifestations usually start during childhood, but late-onset symptoms have been described. Objectives: We aimed to identify additional novel non-synonymous variants (NSV) in COPA and to describe their associated phenotypes in two distinct adult biobanks. Methods: All individuals from two large institutional biobanks (Massachusetts General Brigham, [MGB] and Mayo Clinic Biobank) with available whole-exome sequencing (WES) data were included in this study. All NSV in the 7th, 8th, 9th, and 10th exons of COPA were identified and annotated according to allele frequency reported in GnomAD and functional predictive tools (SIFT, PLOYPHEN-2 and CADD score). Individuals with COPA NSV were matched to controls without COPA NSV from the same biobank by year of birth, sex, and race and ethnicity. Medical record review was performed in cases and controls to collect personal and familial medical history, results of autoantibodies, and chest computed tomography (CT) imaging results obtained through routine clinical care. Associations with the presence of COPA NSV were tested using logistic regression for categorical outcomes or an univariable linear regression for continuous outcomes. Functional impact of ten novel COPA NSV was tested by measuring type-1 IFN activity in cells transfected with mutant plasmids. Results: Among the 106,365 individuals with available WES data, 33 unique NSV were identified among a total of 85 individuals (0.08%); 59 were women (69.4%) and mean age at medical record review was 60.6 years (SD 18.5). Two individuals carried a NSV previously implicated in COPA syndrome (p.R281Q and p.R233H) but none of them had been clinically diagnosed with COPA syndrome. All other identified SNV were rare: 14 recorded rare variants (all with a frequency <0.0001%) and 19 have never been previously described. NSV were predicted as deleterious by both SIFT and POLYPHEN-2 for 16 variants (48%). A history of autoimmune or inflammatory disease was reported for 38/85 carriers (45%) including 12 with inflammatory arthritis (14%), Table 1. Interstitial lung disease was observed in 11/85 patients (13%). Median age at autoimmune/inflammatory disease onset was 50.3 years IQR [39.5 – 63.5] with only 2 patients having childhood onset. Antinuclear antibodies, anti-CCP, and rheumatoid factor were positive for 15/27, 3/13, and 5/22 patients, respectively. A family history of autoimmune/inflammatory disease was reported in 17/68 patients (25%). When compared to matched controls, having a COPA NSV was significantly associated with increased risk for any autoimmune or inflammatory disease (OR = 1.73, 95%CI 1.05 to 2.87, p=0.03), any inflammatory arthritis (OR = 2.83, 95%CI 1.24 to 6.40, p=0.01), any ILD (OR = 6.17, 95%CI 2.27 to 18.4, p<0.001), and having a family history of an autoimmune or an inflammatory disease (OR = 2.81, 95%CI 1.41 to 5.52, p=0.003), Table 1. Functional impact was confirmed for 2/10 of the novel COPA NSV, with a lower type-1 IFN activity compared to two COPA NSV previously as pathogenic mutations, Figure 1. Conclusion: We identified 31 novel NSV beyond the known causal mutations for COPA syndrome and described associations with autoimmune and/or inflammatory diseases, including ILD, most of which were adult-onset. In addition to the COPA syndrome, COPA variants could contribute to the genetic architecture of a spectrum of unrecognized adult-onset inflammatory and autoimmune diseases including ILD. REFERENCES: NIL. Acknowledgements: Société Française de Rhumatologie, NIH, NIAMS. Disclosure of Interests: None declared.

RATIONALEPatients with chronic obstructive pulmonary disease (COPD) and type 2 diabetes (T2D) have worse clinical outcomes compared to patients without metabolic dysregulation. Glucagon-like peptide 1 receptor agonists (GLP-1RA) reduce asthma exacerbation risk and improve forced vital capacity in COPD.OBJECTIVESTo determine whether GLP-1RA use is associated with reduced COPD exacerbation rates, and severe and moderate exacerbation risk, compared with other T2D therapies.METHODSRetrospective, observational, electronic health records-based study using an active comparator, new-user design of 1,642 patients with COPD at a U.S. health system (2012-2022). The COPD cohort was identified using a previously validated machine learning algorithm that includes a natural language processing tool. Exposures were defined as a prescription for GLP-1RAs (reference group), dipeptidyl-peptidase 4 inhibitors (DPP-4i), sodium-glucose cotransporter 2 inhibitors (SGLT2i) or sulfonylureas.MEASUREMENTS AND MAIN RESULTSUnadjusted COPD exacerbation counts were lower in GLP-1RA users. Adjusted exacerbation rates were higher in DPP-4i [IRR,1.48; 95% CI, (1.08 to 2.04); P=.02] and sulfonylurea [IRR, 2.09; 95% CI, (1.62 to 2.69); P <.0001] users compared to GLP-1RA users. GLP-1RA use was associated with significantly reduced risk of severe exacerbations compared to DPP-4i and sulfonylurea users, and of moderate exacerbations compared to sulfonylurea users. After adjustment for clinical covariates, moderate exacerbation risk was also lower in GLP-1RA compared to DPP-4i users. No significant difference in exacerbation outcomes was seen between GLP-1RA and SGLT2i users.CONCLUSIONSProspective studies of COPD exacerbations in patients with comorbid T2D are warranted. Additional study may elucidate mechanisms underlying observed associations with T2D medications.

Le syndrome COPA est une maladie monogénique auto-inflammatoire/auto-immune rare et sévère avec pénétrance variable, affectant le plus fréquemment les poumons et les articulations. Il est cause par des mutations dominantes délétères localisées entre le 7e et le 10e exons du gène COPA (hot-spot), codant pour la sous-unité alpha du complexe COP1, impliquée dans le transport protéique entre l'appareil de Golgi et le réticulum endoplasmique. Le syndrome COPA est associé à une auto-immunité et une hyperactivation des voies interféron de type 1 et lymphocytaires T helper 17. Les manifestations cliniques débutent habituellement dans l'enfance mais des débuts tardifs ont été décrits avec 30 % de porteurs asymptomatiques. Notre objectif était d'identifier de nouveaux variants non-synonymes (VNS) au sein de COPA et de décrire les phénotypes associés. Nous avons inclus l'ensemble des individus provenant d'une large biobanque institutionnelle ayant des données de séquençage d'exome disponibles. Les VNS du 7e au 10e exons du gène COPA ont été identifiés et annotés à l'aide de leur fréquence allélique au sein d'une base de données en ligne (gnomAD) et des scores fonctionnels prédictifs habituels (SIFT, POLYPHEN-2, CADD score). Les dossiers médicaux des porteurs de VNS ont été relus afin de recueillir les antécédents personnels et familiaux, ainsi que les résultats des dosages d'auto-anticorps et d'imageries thoraciques. Parmi les 53 364 adultes avec données de séquençage d'exome disponibles, 24 VNS uniques ont été identifiés au sein du hot-spot de COPA chez 48 individus (prévalence 0,09 %), 33 femmes (69 %), âge médian lors du recueil de donnée 51,7 ans (IQR 39,6–64,5). Un seul des VNS identifies (p.R281Q) avait déjà été rapporté comme une mutation responsable d'un syndrome COPA. La patiente portant ce VNS a développé une polyarthrite rhumatoïde (PR) à l'âge de 18 ans et une hépatite auto-immune à l'âge de 35 ans. Parmi les autres VNS, 15 avaient une fréquence < 0,0001 et 8 n'avaient jamais été identifiés auparavant. Un impact délétère était prédit par SIFT et POLYPHEN-2 pour 12 VNS (50 %). Une maladie auto-immune ou auto-inflammatoire était rapportée chez 21 des 48 porteurs (44 %) avec un âge médian au diagnostic de 42,9 ans (IQR 28,2–56,0) et un début à l'âge adulte chez 19/21 porteurs (91 %). Une atteinte articulaire inflammatoire était décrite chez 6 patients dont 4 PR et une atteinte pulmonaire interstitielle chez 5 patients. Un psoriasis ou une maladie de Crohn étaient rapportés chez 5 patients. Des anticorps antinucléaires, des anti-CCP et un facteur rhumatoïde étaient positifs chez respectivement 9/15, 3/8 et 4/9 des patients. Une histoire familiale de maladie auto-inflammatoire/auto-immune était rapportée chez 9 patients. Deux individus étaient décédés de cardiopathies aiguës sur chroniques à l'âge de 64 et 79 ans. Dans cette première étude des VNS de COPA au sein d'une importante biobanque, nous avons identifié 23 nouveaux VNS non précédemment associés au syndrome COPA. Près de la moitié des porteurs présentaient des maladies auto-inflammatoires ou auto-immunes variées dont la plupart avaient débutées à l'âge adulte. Si l'impact fonctionnel de ces VNS est confirmé, les VNS rares et exoniques de COPA pourraient contribuer à l'architecture génétique de plusieurs maladies communes auto-immunes ou inflammatoires.

Background: Patients with rheumatoid arthritis (RA) are at increased risk of serious infections, with considerable excess morbidity and mortality after pneumonia. RA-related autoantibodies such as anti-cyclic citrullinated peptide (CCP) and rheumatoid factor (RF) may be generated at inflamed pulmonary mucosa prior to clinical RA onset. Therefore, patients with seropositive RA may be at increased risk for pneumonia after RA diagnosis due to subclinical pulmonary injury. Objectives: We investigated whether seropositive RA was associated with increased pneumonia risk compared to seronegative RA. Methods: We performed a retrospective cohort study among RA patients seen at a health care system in Boston, MA. RA patients were identified using a previously validated electronic health record (EHR) algorithm incorporating billing codes, natural language processing (NLP) of notes, medications, and laboratory results at 97% specificity 1 . We constructed an incident RA cohort using NLP for the index date of initial mention of RA. All patients were required to have both CCP and RF data from clinical care to determine serologic RA phenotype. We used semi-supervised machine learning approaches to identify pneumonia using billing codes and terms extracted using NLP, with the Centers for Disease Control definition of pneumonia from medical record review as a gold standard. The area under the receiver operating curve (AUROC) for this billing code+NLP pneumonia algorithm was 0.94 compared to the standard rule-based pneumonia algorithm (billing code on inpatient discharge) AUROC of 0.86 (p<0.001). Smoking status was extracted using NLP methods. Other covariates, including a previous validated weighted RA multimorbidity score 2 , were determined using structured EHR data. We used Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for pneumonia adjusting for potential confounders. Results: We analyzed a total of 4,110 patients with incident RA and both CCP/RF data available. Mean age at index date was 53.0 years (SD 14.8), 77.2% were female, and 79.8% were CCP+ or RF+. During 32,248 patient-years of follow-up (mean 7.8 years/patient), we identified 240 pneumonia cases. Patients with seropositive RA had a HR of 1.99 (95%CI 1.30-3.01, Table) for pneumonia compared to patients with seronegative RA, adjusted for age, sex, smoking, index year, ESR level, glucocorticoid use, DMARD use, and weighted RA multimorbidity score. While CCP+ RA (HR 1.91, 95%CI 1.23-2.97) and RF+ RA (HR 2.07, 95%CI 1.35-3.16) had increased pneumonia risk compared to seronegative RA, the CCP+RF- RA subgroup had no association with pneumonia (HR 0.67, 95%CI 0.23-1.93). Conclusion: Patients with incident seropositive RA, particularly RF+ RA, had increased risk for pneumonia throughout the RA disease course that was not explained by measured confounders including smoking status, multimorbidity, medications, and ESR level. Further studies should investigate how RF+ may predispose RA patients to later develop pneumonia after clinical RA diagnosis. References: [1]Liao KP, Cai T, Gainer V, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res. 2010;62(8):1120–1127. [2]Radner H, Yoshida K, Mjaavatten MD, et al. Development of a multimorbidity index: Impact on quality of life using a rheumatoid arthritis cohort. Semin Arthritis Rheum. 2015;45(2):167–173. Disclosure of Interests: Jeffrey Sparks Consultant of: Bristol-Myers Squibb, Optum, Janssen, Gilead, Weixing Huang: None declared, Bing Lu: None declared, Sicong Huang: None declared, Andrew Cagan: None declared, Vivian Gainer: None declared, Sean Finan: None declared, Guergana Savova: None declared, Daniel Solomon Grant/research support from: Funding from Abbvie and Amgen unrelated to this work, Elizabeth Karlson: None declared, Katherine Liao: None declared

Background:Heterogeneity in disease populations complicates discovery of risk factors. To identify risk factors for subpopulations of diseases, we need analytical methods that can deal with unidentified disease subgroups.Objectives:Inspired by successful approaches from the Big Data field, we developed a high-throughput approach to identify subpopulations within patients with heterogeneous, complex diseases using the wealth of information available in Electronic Medical Records (EMRs).Methods:We extracted longitudinal healthcare-interaction records coded by 1,853 PheCodes[1] of the 64,819 patients from the Boston’s Partners-Biobank. Through dimensionality reduction using t-SNE[2] we created a 2D embedding of 32,424 of these patients (set A). We then identified distinct clusters post-t-SNE using DBscan[3] and visualized the relative importance of individual PheCodes within them using specialized spectrographs. We replicated this procedure in the remaining 32,395 records (set B).Results:Summary statistics of both sets were comparable (Table 1).Table 1.Summary statistics of the total Partners Biobank dataset and the 2 partitions.Set-Aset-BTotalEntries12,200,31112,177,13124,377,442Patients32,42432,39564,819Patientyears369,546.33368,597.92738,144.2unique ICD codes25,05624,95326,305unique Phecodes1,8511,8531,853We found 284 clusters in set A and 295 in set B, of which 63.4% from set A could be mapped to a cluster in set B with a median (range) correlation of 0.24 (0.03 – 0.58).Clusters represented similar yet distinct clinical phenotypes; e.g. patients diagnosed with “other headache syndrome” were separated into four distinct clusters characterized by migraines, neurofibromatosis, epilepsy or brain cancer, all resulting in patients presenting with headaches (Fig. 1 & 2). Though EMR databases tend to be noisy, our method was also able to differentiate misclassification from true cases; SLE patients with RA codes clustered separately from true RA cases.Figure 1.Two dimensional representation of Set A generated using dimensionality reduction (tSNE) and clustering (DBScan).Figure 2.Phenotype Spectrographs (PheSpecs) of four clusters characterized by “Other headache syndromes”, driven by codes relating to migraine, epilepsy, neurofibromatosis or brain cancer.Conclusion:We have shown that EMR data can be used to identify and visualize latent structure in patient categorizations, using an approach based on dimension reduction and clustering machine learning techniques. Our method can identify misclassified patients as well as separate patients with similar problems into subsets with different associated medical problems. Our approach adds a new and powerful tool to aid in the discovery of novel risk factors in complex, heterogeneous diseases.References:[1] Denny, J.C. et al. Bioinformatics (2010)[2]van der Maaten et al. Journal of Machine Learning Research (2008)[3] Ester, M. et al. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. (1996)Disclosure of Interests:Marc Maurits: None declared, Thomas Huizinga Grant/research support from: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Consultant of: Ablynx, Bristol-Myers Squibb, Roche, Sanofi, Marcel Reinders: None declared, Soumya Raychaudhuri: None declared, Elizabeth Karlson: None declared, Erik van den Akker: None declared, Rachel Knevel: None declared

These include: References http://ard.bmj.com/content/early/2010/03/12/ard.2009.120170.full.html#ref-list-1 This article cites 45 articles, 12 of which can be accessed free at: P<P Published online March 16, 2010 in advance of the print journal. service Email alerting box at the top right corner of the online article. Receive free email alerts when new articles cite this article. Sign up in the Notes