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. 2023 Feb 28;5(4):201–226. doi: 10.1002/acr2.11535

Peripheral Blood Biomarkers for Rheumatoid Arthritis–Associated Interstitial Lung Disease: A Systematic Review

Daniel Van Kalsbeek 1, Rebecca Brooks 1, Dawson Shaver 2, Ariadne Ebel 3, Daniel Hershberger 1, Cynthia Schmidt 1, Jill A Poole 1, Dana P Ascherman 4, Geoffrey M Thiele 5, Ted R Mikuls 5, Bryant R England 5,✉
PMCID: PMC10100703  PMID: 36852564

Abstract

Background

Biomarkers have been proposed as tools to aid in the identification and prognostication of interstitial lung disease (ILD) in rheumatoid arthritis (RA). We performed a systematic review of studies evaluating peripheral blood biomarkers and their association with RA‐ILD and its prognosis.

Methods

Medline, Embase, the Cochrane Library, and Scopus were queried for relevant studies, with the final search update on July 12, 2021. We included studies evaluating peripheral blood biomarkers for the identification and/or prognostication of RA‐ILD, extracting the performance of individual biomarkers for identifying RA‐ILD, and predicting prognosis. Modified versions of the Quality Assessment of Diagnostic Accuracy Studies 2 and the Quality in Prognosis Studies tools were used for quality assessment.

Results

Seventy studies met eligibility criteria. Study and patient characteristics, analytical methods, strength and consistency of associations, and study quality were heterogeneous. A total of 92 biomarkers were positively associated and 12 were negatively associated with RA‐ILD among patients with RA in one or more report. Only a small number of biomarkers were evaluated in multiple cohorts using adjusted analyses. Biomarkers most strongly associated with RA‐ILD overlapped with those identified for idiopathic pulmonary fibrosis. Few prognostic biomarkers of RA‐ILD were identified.

Conclusion

Several peripheral blood biomarkers are associated with the presence of RA‐ILD, but few have been assessed in multivariable models, have been externally validated, have discriminated RA‐ILD from other lung disease, or have prognosticated the disease course. High‐quality studies investigating and validating peripheral biomarkers in RA‐ILD are needed before they can be employed in clinical care.

INTRODUCTION

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by small joint synovitis that is estimated to affect 0.3% to 1% of the global population (1, 2). Extraarticular disease may occur in up to 50% of patients with RA, with pulmonary manifestations among the most common extraarticular features. Interstitial lung disease (ILD) clinically affects up to 10% of patients with RA and subclinically affects up to 40% (3, 4, 5, 6, 7). The pathogenesis of RA‐associated ILD (RA‐ILD) is complex and incompletely understood, with evidence supporting autoimmunity, dysregulated inflammatory and fibrotic pathways, and oxidative stress in a genetically susceptible host (8, 9). RA‐ILD typically presents as a usual interstitial pneumonia (UIP) or nonspecific interstitial pneumonia pattern (3, 4, 10, 11, 12, 13). Diagnosis requires a thorough evaluation, including laboratory and imaging studies; pulmonary function testing (PFT); medication review; occasionally, tissue histopathology; and frequently, multidisciplinary input. Prompt recognition of RA‐ILD is crucial because it warrants close monitoring and often necessitates therapeutic alterations (4, 14, 15, 16). Although overall survival of patients with RA has improved over the last few decades, respiratory disease remains one of the leading causes of death, and RA‐ILD is associated with a marked reduction in survival compared with RA without ILD, illustrating the need for improved diagnostic and therapeutic modalities (17, 18, 19).

Biomarkers are a promising area of investigation in RA‐ILD because they have the potential to advance pathophysiologic understanding and elucidate therapeutic targets, improve disease identification and diagnostic accuracy, facilitate prognostication, and inform treatment decisions. Although imaging and histopathology have important roles that are unlikely to be replaced by biomarkers, they can be nonspecific in RA‐ILD, can be impractical to obtain in certain settings, and cannot be scaled for population health efforts (8, 12, 14). Peripheral blood biomarkers, which are more practical in both clinical and population health settings, are of particular interest. Numerous biomarkers, including autoantibodies, cytokines, lung epithelial–related proteins (eg, surfactant proteins and mucins), and genetic polymorphisms, have been recognized as candidates for identifying and/or prognosticating RA‐ILD (20, 21). Although promising, the evidence supporting these biomarkers has not been rigorously assessed, which is necessary before they can be used clinically.

We aimed to perform a systematic literature review of peripheral blood biomarkers in RA‐ILD. Our objectives were to synthesize the evidence concerning these biomarkers’ ability to 1) differentiate RA‐ILD from RA without ILD, 2) differentiate RA‐ILD from other lung diseases, and 3) prognosticate RA‐ILD disease course.

MATERIALS AND METHODS

We conducted a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines (22). We registered the systematic literature review protocol with PROSPERO (identifier: CRD42019137143), an international prospective registry of systematic reviews.

Search strategy

A medical librarian (CS) searched Medline (via EBSCOhost), the Cochrane Library (via Wiley), Embase (1974‐present version via embase.com), and Scopus. She also searched PubMed for recently indexed records and unindexed records because these appear in PubMed earlier than they appear in Medline via EBSCOhost. The initial searches were conducted on April 25, 2019, and several search updates were performed. The last search update took place on July 12, 2021.

Both keywords and subject headings (when available) were used for each of the following three search concepts: RA, ILD, and biomarkers. The terms used for the biomarker concept included terms (subject headings and keywords) for the general biomarker concept as well as terms for the individual potential RA‐ILD‐associated biomarkers that had been identified by pilot searches. A few additional biomarkers were identified by the first attempt at an exhaustive search. Terms for these biomarkers were added to the search strategies. Because no funds were available for translation, English‐language filters were applied to the searches. Publication‐type filters were used to remove editorials, review articles, and conference abstracts. Publication‐type filters were used (when available) to separate the remaining records into three groups: 1) systematic reviews and meta‐analyses, 2) case reports, and 3) other articles. Complete search strategies are included in Supplementary Material 1.

A total of 1604 records were retrieved by the initial search and search updates. This total included 34 records from the Cochrane Library, 485 records from Embase, 435 records from EBSCOhost Medline, 181 records from PubMed, and 469 records from Scopus. The RefWorks duplicate detection tool was used to identify and remove 690 duplicate records. Records for 914 unique publications remained for review. We also reviewed the reference lists of other pertinent systematic reviews and meta‐analyses and identified 10 additional potentially relevant studies.

Study selection

We included the results of published literature on peripheral blood biomarkers in the English language. Case reports, case series, studies with less than 20 participants with RA‐ILD, review articles, practice guidelines, systematic reviews, meta‐analyses, editorials, articles not reporting results specifically for RA‐ILD (eg, combining RA‐ILD with other connective tissue disease–associated ILD), and articles published prior to 1990 (because of differences in the assessment of ILD during this time period) were excluded. Four authors (AE, DS, DVK, and RB) independently reviewed titles, abstracts, and full texts to determine eligibility for inclusion. Disagreements were settled by a third reviewer (BRE). Full texts of the included studies were stored in an EndNote library.

Data extraction

Three authors (DS, DVK, and RB) extracted relevant study data, including study characteristics, patient characteristics, and the performance of the biomarker(s) studied. Training in data abstraction was performed by abstracting one study in tandem and then abstracting five studies in duplicate and comparing results. Study characteristics extracted consisted of country(ies) of study participants, study design, sample size (including the number with RA‐ILD), sample handling, method of biomarker measurement, inclusion and exclusion criteria, and criteria for RA and ILD diagnosis. Patient characteristics extracted included age, sex, race and/or ethnicity, smoking status, RA and ILD disease severity, RA disease duration, and serological status. Study outcomes were collected for identification of RA‐ILD compared with RA without ILD, identification of RA‐ILD compared with other lung diseases, and the prognostication of RA‐ILD (PFT and/or radiographic progression, respiratory events, and all‐cause or respiratory‐related mortality). Abstracted data were organized in an Excel spreadsheet. Performance data across studies were summarized descriptively for each biomarker. Heterogeneity in these results precluded a formal meta‐analysis. When studies performed both adjusted and unadjusted analyses for a given biomarker, results of the adjusted analysis were reported. Because terminology varied across studies, we used RA‐ILD to encompass RA‐associated interstitial pneumonia and RA‐associated pulmonary fibrosis. Because of variability between studies, the following terms were used interchangeably unless otherwise specified: titers and concentration; anti–cyclic citrullinated peptides (anti‐CCPs) and anti–citrullinated protein antibodies.

Quality assessment

Study quality was assessed by three authors (DS, DVK, and RB) using a modified Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS‐2) tool for diagnostic studies and the Quality in Prognosis Studies (QUIPS) tool for prognostic studies (23, 24). We added a statistical analysis domain to the QUADAS‐2 tool to enable a more thorough assessment for risk of bias related to confounding and considered the risk of bias to be low if analyses were adjusted for appropriate confounders (eg, age, sex, smoking history, and RA disease characteristics) and high if analyses were unadjusted. Risk of bias was reported as unclear, low, or high for studies being evaluated with QUADAS‐2 and as low, moderate, or high for studies being evaluated with QUIPS. Training in quality assessment was done using the same method as for training in data abstraction.

RESULTS

Study identification and study characteristics

The literature search revealed 924 articles after deduplication, with 70 meeting inclusion criteria (Supplementary Figure 1). Included studies were from 12 different countries and most often used a cross‐sectional study design (Table 1). RA was typically classified using the 1987 American College of Rheumatology (ACR) criteria or the 2010 ACR/European League Against Rheumatism (EULAR) criteria. ILD assessment was variable; although many studies used chest computed tomography (CT), they differed in the number and type of assessors interpreting the CT findings, the type of CT performed, and whether other criteria were employed, such as PFTs, specialist diagnoses, multidisciplinary discussion, or review of medical records. The majority of studies analyzed serum samples, with enzyme‐linked immunosorbent assay being the most common biomarker measurement method. Sample handling processes were rarely specified.

Table 1.

Study characteristics

Author, year (citation) Country Study design Biomarkers Sample type/measurement method Inclusion/exclusion criteria ILD ascertainment
Abdel‐Wahab et al, 2016 (52) Egypt Cross‐sectional IL‐33 Serum/ELISA 1987 ACR criteria/liver disease, prior anaphylaxis or ischemia, other rheumatologic disease HRCT
Alunno et al, 2018 (53) Italy Cross‐sectional Anti‐CEP‐1 Serum/ELISA 2010 ACR/EULAR criteria/not specified CXR, HRCT
Alunno et al, 2018 (54) Italy Cross‐sectional Anti‐CEP‐1 Serum/ELISA 1987 ACR criteria/not specified Clinical and radiological records
Avouac et al, 2020 (38) France Cross‐sectional Anti‐CCP, KL‐6, CCL18, SP‐D Serum/ELISA 1987 ACR or 2010 ACR/EULAR criteria/not specified HRCT by one to two blinded investigators
Bao et al, 2021 (55) China Cross‐sectional CA‐19‐9, CA‐125, CEA, CA‐153, CYFRA21‐1 Serum/not specified RA, SS, SLE, IM, SSc, or MCTD/overlap syndromes, multiple autoimmune diseases, malignancy, sarcoidosis, amyloidosis, severe infection, and/or severe liver or kidney disease HRCT by one blinded investigator
Castellanos‐Moreira et al, 2020 (56) Spain Cross‐sectional Anti‐FCS IgG, anti‐Fib IgG, anti‐CFFHP IgG, anti‐FCS IgA, RF, anti‐CCP Serum/ELISA, nephelometry, chemiluminescent immunoassay 2010 ACR/EULAR criteria/other inflammatory arthritis or CTD HRCT confirmation by multidisciplinary committee
Chen et al, 2015 (30) China, USA Cross‐sectional RF, anti‐CCP, MMP‐7, IP‐10 Serum/ELISA 1987 ACR criteria/indeterminate CT findings HRCT agreement between two blinded reviewers, PFT
Chen et al, 2019 (57) China Cross‐sectional Platelet/lymphocyte ratio, lymphocyte/monocyte ratio, neutrophil/lymphocyte ratio, ESR Serum/automatic blood counting system 1987 ACR or 2010 ACR/EULAR criteria/cancer, severe infections, cardiovascular disease, systemic blood disease, or other diseases HRCT
Correia et al, 2019 (58) USA Cross‐sectional Anti‐CCP Serum/ELISA 2010 ACR/EULAR criteria or RA diagnosed by rheumatologist/other rheumatologic disease, occupational exposure, or thoracic radiation Clinical diagnosis by pulmonologist, CT, PFT
Darrah et al, 2018 (59) USA Cross‐sectional Anti‐PAD2 Serum/ELISA ESCAPE cohort, 1987 ACR criteria/not specified MDCT by blinded radiologist
Del Angel‐Pablo et al, 2020 (60) Mexico Case‐control Anti‐HLA class II, LABScreen PRA, ESR, CRP, anti‐CCP Serum/fluorescence immunoassay 2010 ACR/EULAR criteria, ATS or ERS IIP criteria/not specified HRCT
Doyle et al, 2015 (31) USA Cross‐sectional RF, anti‐CCP, MMP‐7, CCL18 (PARC), SP‐D Serum/ELISA, custom multiplex bead array assay BRASS or ACR cohort, fulfilling ACR criteria/indeterminate or uninterpretable imaging HRCT
England et al, 2019 (61) USA Cross‐sectional Anti‐MAA antibody Serum/ELISA VARA registry, 1987 ACR criteria/RA‐ILD development >2 years after registry enrollment Medical record review: clinical diagnosis, imaging findings, and histopathology
Fadda et al, 2018 (62) Egypt Cross‐sectional RF, anti‐CCP Serum/nephelometry, ELISA 2010 ACR/EULAR criteria/TB, hepatitis C, overlap syndromes, methotrexate pneumonitis HRCT, PFT
Fotoh et al, 2021 (39) USA Case‐control KL‐6, ESR, CRP Serum/latex‐enhanced immunoturbidimetric assay method, ELISA 2010 ACR criteria, age > 18 years, duration >3 years/pneumonia, multiple autoimmune diseases, heart failure, pulmonary surgery, respiratory infections, asthma, COPD, lung cancer, renal failure, and pregnancy HRCT by same radiologist, PFT analysis according to ATS
Fu et al, 2018 (63) China Cross‐sectional LOXL2 Serum/ELISA 2010 ACR/EULAR criteria/other autoimmune, infectious, or neoplastic disease; lung surgery HRCT reviewed by experienced work group
Fujita et al, 2020 (64) Japan Case‐control GAL‐9, ACPA, RF, anti‐CCP, MMP‐3, ESR, CRP Serum/ELISA 2010 ACR/EULAR criteria/overlapping syndromes HRCT by one blinded radiologist
Furukawa et al, 2012 (34) Japan Cross‐sectional RF, Anti‐CCP, shared epitope Not specified/latex agglutination, ELISA, PCR 1987 ACR criteria/other CTD, non‐ILD findings on imaging HRCT or CT reviewed by two specialized physicians
Furukawa et al, 2013 (65) Japan Cross‐sectional RF, LDH, CRP, KL‐6, SP‐D, amino acids Plasma/not specified 1987 ACR criteria/steroid administration (≥15 mg/day prednisolone equivalent) or unavailable imaging HRCT or CT with agreement by two specialized physicians
Furukawa et al, 2020 (66) Japan Case‐control Metabolomic profiles, RF, ACPA, KL‐6, SP‐D Serum/nephelometry, ELISA, electrochemiluminescence immunoassay, capillary electrophoresis time‐of‐flight mass spectrometry 1987 ACR or 2010 ACR/EULAR criteria, CT images/not specified CT or HRCT reviewed by two specialists in RA‐ILD
Giles et al, 2014 (67) USA Cross‐sectional RF, anti‐CCP, anti‐PAD3/4XR, CRP, IL‐6 Serum/custom radioimmunoassay ESCAPE RA cohort, 1987 ACR criteria/not specified MDCT by blinded radiologist
Giles et al, 2014 (68) USA Cross‐sectional RF, anti‐CCP, shared epitope alleles, CRP, IL‐6, specific ACPAs, anti‐Fib A, anti‐hsp60, anti‐apolipoprotein A1, anti‐apolipoprotein E Serum/ELISA, nephelometry, custom Bio‐Plex bead array ESCAPE RA cohort, 1987 ACR criteria/not specified HRCT by blinded pulmonary radiologist
Harlow et al, 2013 (69) USA Cross‐sectional Anti‐cit‐hsp90 Serum/ELISA 1987 ACR criteria/not specified Radiograph, CT, PFT
Hillarby et al, 1993 (70) UK Cross‐sectional HLA‐DQ and HLA‐DR, C4 allotype Serum, plasma/PCR, electrophoresis, immunofixation 1958 revised criteria/heart failure or pneumoconiosis Clinical and radiographic findings
Hussein et al, 2021 (71) Egypt Cross‐sectional RF, anti‐CCP, ESR, CRP, IL‐13, KL‐6, SP‐D Serum/Westergren method, ELISA, real‐time quantitative PCR 2010 ACR/EULAR criteria/chronic chest disorders (asthma, COPD), TB, CHF Pulmonologist diagnosis and two of the following three: CXR or CT, restrictive pattern PFTs, and/or lung biopsy
Juge et al, 2018 (32) Multinational Cross‐sectional RF, anti‐CCP, MUC5B rs35705950 promoter variant Not specified/genotyping assay, PCR 1987 ACR or 2010 ACR/EULAR criteria/not specified HRCT
Kass et al, 2019 (29) USA Cross‐sectional Numerous cytokines, growth factors, remodeling proteins Serum/multiplex ELISA 1987 ACR criteria/not specified Radiograph, HRCT
Kelly et al, 2014 (72) UK Case‐control RF, anti‐CCP Serum/not specified 2010 ACR/EULAR criteria/not specified HRCT
Kim et al, 2020 (40) Korea Case‐control KL‐6, RF Plasma/latex‐enhanced immunoturbidimetric assay 2010 ACR criteria, blood sample availability/not specified HRCT, pathologic findings
Lai et al, 2019 (73) China Cross‐sectional RF, ACPA, ESR, CRP IgG, IgA, IgM, NK cells, T cells, B cells Not specified/flow cytometry 1987 ACR criteria/other chronic respiratory disease, TB, lung nodules or tumors, other major organ dysfunction, heavy smoking or alcohol intake HRCT
Lee et al, 2016 (41) South Korea Retrospective cohort IL‐6, IL‐32, KL‐6, MMP‐7, SP‐A Plasma/ELISA RA diagnosed by rheumatologist, fulfillment of revised ACR criteria/other rheumatologic disease, drug‐induced lung disease, or < 1 year follow‐up Multidisciplinary team, HRCT, histopathology
Lee et al, 2019 (45) South Korea Cross‐sectional KL‐6 Serum/immunoturbidimetry Not specified/patients with overlapping syndromes or multiple autoimmune diseases HRCT, PFT
Ma et al, 2019 (74) China Cross‐sectional RF, CXCL16 Serum/ELISA 2010 ACR/EULAR criteria/infectious, cancer, and metabolic diseases HRCT, PFT
Maniwa et al, 2000 (75) Japan Cross‐sectional IL‐1a Serum/radioimmunoassay 1987 ACR criteria/not specified Imaging, PFT, histopathology
Matsuo et al, 2019 (76) Japan Cross‐sectional RF, CCP, IL‐16, ANA, KL‐6, MMP‐3 Serum/ELISA KURAMA cohort, 1987 ACR or 2010 ACR/EULAR criteria/not specified CT
Matsushita et al, 2017 (77) Japan Cross‐sectional RF, anti‐CCP, anti‐aaRS antibody Serum/latex agglutination, ELISA, line blot test kit 2010 ACR/EULAR criteria/not specified Histopathology, CXR, HRCT
Mori et al, 2012 (78) Japan Cross‐sectional HLA‐DRB1 alleles, RF, anti‐CCP Serum/ELISA, nephelometry, PCR 1987 ACR criteria/history of occupational exposure or thoracic radiation HRCT
Nakajima et al, 2000 (79) Japan Cross‐sectional KL‐6, CRP, LDH Serum/ELISA 1987 ACR criteria/other pulmonary disease or malignancy Clinical diagnosis, CT, PFT
Natalini et al, 2021 (80) USA Cross‐sectional and retrospective cohort RF, ACPA Serum/ELISA, nephelometry VARA registry, 1987 ACR criteria/not specified Provider diagnosis of ILD and CT evidence or lung biopsy
Newton et al, 2019 (37) USA Cross‐sectional MUC5B rs35705950, TOLLIP rs5743890, leukocyte telomere length Peripheral blood leukocytes/PCR, SNP genotyping assays Rheumatologic evaluation/not specified HRCT, histopathology
Oka et al, 2016 (35) Japan Cross‐sectional RF, CCP, KL‐6, HLA‐DR2, HLA‐DR4, shared epitope Not specified/latex agglutination, ELISA, PCR 1987 ACR criteria, cross‐sectional imaging/history of occupational exposure, thoracic radiation, or other predominant imaging finding HRCT agreement between two specialized radiologists
Oka et al, 2017 (81) Japan Cross‐sectional SP‐D, KL‐6, miRNAs Plasma/ELISA, RT‐PCR 1987 ACR criteria/not specified HRCT with agreement between two specialized physicians
Pulito‐Cueto et al, 2020 (82) Spain Cross‐sectional CRP, ESR, RF, ACPA, endothelial progenitor cells (CD34+, CD45low, CD309+, and CD133+) Peripheral venous blood/flow cytometry 2010 ACR/EULAR criteria/not specified HRCT by radiologist, ATS/ERS ILD criteria
Ren et al, 2021 (83) China Cross‐sectional ESR, CRP, IgG, IgM, IgA, CTGF Serum/ELISA 2010 ACR/EULAR criteria or early RA criteria (defined by three of the following: morning stiffness ≥30 min, arthritis of ≥3 joints, arthritis of hands, and positive RF) HRCT by radiologist, ATS/ERS ILD criteria
Restrepo et al, 2015 (84) USA Cross‐sectional RF, anti‐CCP, shared epitope Serum/ELISA, PCR 1987 ACR criteria/history of other pulmonary disease Medical record review, clinical diagnosis, imaging, PFT, histopathology
Rocha‐Muñoz et al, 2015 (85) Mexico Cross‐sectional RF, anti‐CCP Serum/ELISA 1987 ACR criteria/other pulmonary disease or psychiatric disorder HRCT, PFT
Saku et al, 2021 (42) Japan Case‐control Monocytes, neutrophils, lymphocyte ratios, KL‐6, SP‐D, CRP, RF Not specified/not specified 1987 or 2010 ACR/EULAR criteria/other causes of pulmonary disorders Clinical presentation, PFTs, or HRCT
Salaffi et al, 2019 (86) Italy Cross‐sectional ESR, CRP, ACPA, RF Not specified 2010 ACR/EULAR criteria/pulmonary infection, pulmonary hypertension, CHF, other significant pulmonary abnormalities on HRCT HRCT by two experienced radiologists
Sargin et al, 2018 (26) Turkey Cross‐sectional RF, CA‐125 Serum/not specified 2010 ACR/EULAR criteria/other connective tissue disease, TB, or pulmonary infection Clinical diagnosis, PFT, imaging
Sargin et al, 2021 (87) Turkey Cross‐sectional Platelet indices, RF, anti‐CCP, ESR, CRP Not specified/Mindray BC‐6800 hematology analyzer 2010 ACR/EULAR criteria/<18 years, other autoimmune diseases, hematologic diseases, antiaggregant medication, malignancy, pulmonary infections HRCT
Shen et al, 2019 (88) China Cross‐sectional Tumor markers, ESR, CRP, complement, immunoglobulins Serum/not specified PM/DM, SSc, CTD, MCTD, RA, SS, or SLE/other severe pulmonary disease (eg, sleep apnea, PAH) HRCT by two blinded expert radiologists
Sherin et al, 2019 (89) Egypt Cross‐sectional RF, vitamin D Serum/latex agglutination, ELISA 2010 ACR/EULAR criteria/not specified HRCT interpreted by radiologist and pulmonologist
Skare et al, 2011 (90) Brazil Cross‐sectional RF, anti‐CCP, ANA Not specified/not specified 1987 ACR criteria/COPD, TB, prior radiation, chest surgery HRCT
Sokai et al, 2016 (46) Japan Cross‐sectional RF, anti‐CCP, KL‐6, SP‐D Not specified/not specified 1987 ACR criteria/no respiratory impedance, PFTs, or CT HRCT evaluated by experienced radiologist
Solomon et al, 2020 (91) USA Cross‐sectional IgA‐ACPA, IgG‐ACPA Serum/ELISA, nephelometry RA (rheumatologist diagnosis), IPF (2018 ATS guidelines), HP (pulmonologist with expertise in ILD and multidisciplinary conference)/RA cohort (atypical mycobacterial infection); others not specified HRCT with UIP or probable UIP evaluated by two independent expert thoracic radiologists
Wada et al, 2010 (92) Japan Cross‐sectional RF, ESR, CRP, ANCA Serum/ELISA 1987 ACR criteria/not specified HRCT evaluated by two experienced physicians
Wang et al, 2016 (27) China Cross‐sectional RF, CA‐125, CA‐15‐3, CA‐19‐9 Not specified/not specified 1987 ACR criteria/current or prior neoplasm HRCT with agreement by blinded radiologist and pulmonologist
Wang et al, 2019 (93) China Cross‐sectional TGFβ1 Serum/ELISA 1987 ACR criteria/not specified Not specified
Wang et al, 2020 (33) China Retrospective cohort ESR, RF, ACPA, MUC5B, ABCA3, SFTPC, PARN, RTEL1 Peripheral blood mononuclear cells/whole‐exome sequencing 2010 ACR/EULAR criteria/occupational or environmental exposure, drug use, other known causes of ILD Clinical presentation, PFTs, and HRCT reviewed by radiologists and pulmonologists
Wang et al, 2021 (94) China Cross‐sectional IL‐11 Serum/ELISA 1987 ACR or 2010 ACR/EULAR criteria/infection, malignancy, or other CTD HRCT
Wen et al, 2018 (95) China Cross‐sectional LBP Serum/ELISA 2010 ACR/EULAR criteria/not specified Radiograph, PFT
Wu et al, 2020 (28) China Cross‐sectional sPD‐L1, anti‐CCP, RF, ESR, CRP, ferritin Serum/ELISA 1987 ACR criteria/other ILD causes, chronic pulmonary diseases, infectious diseases, severe heart, lung, and renal dysfunction HRCT and PFT
Xiangyang et al, 2012 (96) China Cross‐sectional IL‐33 Serum/ELISA 1987 ACR criteria/not specified HRCT
Xue et al, 2021 (97) China Retrospective cohort DKK1, CRP Serum/ELISA ACR (year not specified)/not specified 2013 IIP classification (ATS/ERS criteria not specified)
Yang et al, 2019 (98) Korea Case‐control RF, anti‐CCP, ESR, CRP Not specified/immunoturbidimetric assay, chemiluminescent microparticle immunoassay 1987 ACR criteria/not specified 2013 ATS/ERS IIP criteria
Yin et al, 2014 (99) China Cross‐sectional RF, anti‐CCP Serum/ELISA 1987 ACR criteria/ILD prior to RA diagnosis, other chronic pulmonary disease, or incomplete medical record HRCT by blinded radiologist
Yu et al, 2019 (36) China Cross‐sectional RF, CCP, anti‐keratin, ESR, LDH, CRP, Wnt5a Plasma, serum/ELISA 1987 ACR criteria, HRCT scan/uninterpretable or indeterminate imaging, other pulmonary disease, unavailable serum, heart disease, severe heart, lung, or renal impairment HRCT
Zhang et al, 2017 (100) China Retrospective cohort RF, CCP Not specified/not specified 1987 ACR criteria or 2010 ACR/EULAR criteria/other pulmonary disease, chronic liver or kidney disease, rheumatic heart disease, myocardial infarction, or other disease HRCT
Zheng et al, 2021 (25) China Cross‐sectional KL‐6, CA‐19‐9, CA‐125, CEA, RF, anti‐CCP, CRP, ESR Serum/chemiluminescent microparticle immunoassay 1987 ACR criteria/other pulmonary disease, malignancy or diseases that affect tumor markers, recent infection of HIV, viral hepatitis HRCT by two blinded radiologists and a respiratory surgeon
Zhou et al, 2020 (101) China Cross‐sectional lncRNA, RF, anti‐CCP, ESR, CRP Peripheral blood mononuclear cells/PCR, commercial lncRNA microarray 2010 ACR/EULAR criteria/male patients, elderly, smokers, other autoimmune disease, positive ANA, other respiratory disease, tumors, chronic liver or kidney disease, heart disease Clinical, HRCT, and PFT interpreted by two physicians
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Abbreviations: aaRS, aminoacyl–transfer RNA synthetase; ABCA, adenosine triphosphate‐binding cassette sub‐family A; ACPA, anti–citrullinated protein antibody; ACR, American College of Rheumatology; ANA, antinuclear antibody; ATS, American Thoracic Society; BRASS, Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study; CA, cancer or carbohydrate antigen; CCL, chemokine ligand; CCP, cyclic citrullinated peptide; CEA, carcinoembryonic antigen; CEP‐1, citrullinated α‐enolase peptide; CFFHP, chimeric fibrine/filagrine homocitrullinated peptide; CHF, congestive heart failure; cit‐hsp, citrullinated heat shock protein; COPD, chronic obstructive pulmonary disease; CRP, C‐reactive protein; CT, computed tomography; CTD, connective tissue disease; CTGF, connective tissue growth factor; CXCL, CXC chemokine ligand; CXR, chest radiograph; CYFRA21‐1, cytokeratin 19 fragment; DKK, dickkopf; DM, dermatomyositis; ELISA, enzyme‐linked immunosorbent assay; ERS, European Respiratory Society; ESCAPE, Evaluation of Subclinical Cardiovascular Disease and Predictors; ESR, erythrocyte sedimentation rate; EULAR, European League Against Rheumatism; FCS, fetal calf serum; Fib, fibrinogen; GAL, galactin; HIV, human immunodeficiency virus; HLA, human leukocyte antigen; HP, hypersensitivity pneumonitis; HRCT, high‐resolution computed tomography; hsp, heat shock protein; Ig, immunoglobulin; IIP, idiopathic interstitial pneumonia; IL, interleukin; ILD, interstitial lung disease; IM, inflammatory myositis; IP, inducible protein; IPF, idiopathic pulmonary fibrosis; KL‐6, Krebs von den Lungen 6 glycoprotein; KURAMA, Kyoto University Rheumatoid Arthritis Management Alliance; LBP, lipopolysaccharide binding protein; LDH, lactate dehydrogenase; lncRNA, long noncoding RNA; LOXL, lysyl oxidase‐like; MAA, malondialdehyde‐acetaldehyde adducts; MCTD, mixed connective tissue disease; MDCT, multidetector row computed tomography; miRNA, microRNA; MMP, matrix metalloproteinases; MUC5B, mucin5B; NK, natural killer; PAD, peptidyl arginine deiminase; PAH, pulmonary arterial hypertension; PARC, pulmonary and activation‐regulated chemokine; PARN, poly[A]‐specific ribonuclease; PCR, polymerase chain reaction; PFT, pulmonary function test; PM, polymyositis; PRA, panel‐reactive antibodies; RA, rheumatoid arthritis; RF, rheumatoid factor; RTEL1, regulator of telomere elongation helicase 1; RT‐PCR, reverse transcription–polymerase chain reaction; SFTPC, surfactant protein C; SLE, systemic lupus erythematosus; SNP, single‐nucleotide polymorphism; SP‐A, surfactant protein‐A; SP‐D, lung epithelial‐derived surfactant protein D; sPD‐L, soluble programmed death‐ligand; SS, Sjögren syndrome; SSc, systemic sclerosis; TB, tuberculosis; TGFβ1, transforming growth factor β1; TOLLIP, Toll‐interacting protein; UIP, usual interstitial pneumonia; VARA, Veterans Affairs Rheumatoid Arthritis; XR, cross‐reactive.

Sample sizes of individual studies ranged from 60 to 6682 overall, with the number of patients with RA‐ILD ranging from 20 to 620 (Table 2). The mean age of participants was generally in the sixth or seventh decade. Patients were most frequently of White or Asian race. There was a strong female predominance among patients with RA (approximately 60%‐70% female), but this was attenuated in the patients with RA‐ILD (approximately 50%‐60% female). The proportion of patients who had a smoking history varied across studies, but patients with RA‐ILD were consistently more likely to have a history of smoking than patients with RA without ILD. RA disease duration was more than 8 years in most studies. Severity measures for RA and ILD were not consistently reported.

Table 2.

Patient characteristics

Author, year (citation) Sample size Age, mean or median years Race and ethnicity Sex, % female Smoking history, % RA duration, mean or median RA severity ILD severity
Abdel‐Wahab et al, 2016 (52) RA: 50 RA: 51.5 Study site: Egypt RA: 76 Not reported RA: 11.4 y Not reported Not reported
Control: 30 Control: 51
Alunno et al, 2018 (53) RA: 252 All RA: 61.7 Study site: Italy All RA: 77 Not reported RA without ILD: Not reported Not reported
RA‐ILD: 37 12.6 y
Alunno et al, 2018 (54) RA: 100 Total cohort: 62 Study site: Italy Not reported Total: 26 Not reported Erosive disease (%) Not reported
RA‐ILD: 21 Total: 60
Avouac et al, 2020 (38) RA without ILD: 107 RA without ILD: 62 Study sites: France, RA without ILD: 75 RA without ILD: 26 RA without ILD: 12 y Erosions (%) FVC% pred
RA‐ILD: 40 RA‐ILD: 71 Japan, Switzerland RA‐ILD: 55 RA‐ILD: 60 RA‐ILD: 12 y RA without ILD: 63 RA‐ILD: 15% a
RA‐ILD: 50 DLCO% pred
RA‐ILD: 79%, 61%
Bao et al, 2021 (55) RA without ILD: 60 CTD‐ILD: 58.5 Study site: Japan CTD‐ILD: 67.6 Not reported Not reported Not reported Not reported
RA‐ILD: 91 CTD alone: 56.2 CTD alone: 65
SS‐ILD: 19
SLE‐ILD: 13
IM‐ILD: 11
Castellanos‐Moreira et al, 2020 (56) RA‐ILD: 37 RA‐ILD: 67.3 White (%) RA‐ILD: 68 Current, ever RA‐ILD: 11.6 y DAS28 Not reported
RA without ILD: 243 RA without ILD: 57.7 RA‐ILD: 84 RA without ILD: 82 RA‐ILD: 19, 57 RA without ILD: 5.3 y RA‐ILD: 3.7
RA without ILD: 85 RA without ILD: 16, 44 RA without ILD: 2.7
HAQ
RA‐ILD: 0.69
RA without ILD: 0.31
Chen et al, 2015 (30) USA, Chinese USA, Chinese White (%) USA, Chinese USA, Chinese USA, Chinese DAS28 FVC% pred
Total: 86, 133 RA without ILD: 50.3, 43.4 USA cohort RA without ILD: 76, 82 RA without ILD: 42, 12 RA without ILD: 8.4 y, 4.3 y USA, Chinese USA, Chinese
RA without ILD: 22, 50 RA‐ILD: 65.3, 53.0 RA without ILD: 71% RA‐ILD: 37, 71 RA‐ILD: 55, 12 RA‐ILD: 12.8 y, 5.6 y RA‐advanced ILD: 4.4, 3.3 RA without ILD: 101.4, 83.8
RA‐ILD: 49, 41 RA‐ILD: 76% RA‐advanced ILD: 68.1, 72.3
Indeterminate RA‐ ILD: 15, 42 Chinese cohort
Not reported
Chen et al, 2019 (57) RA without ILD: 198 RA without ILD: 59.8 Study site: China RA without ILD: 80.3 Not reported Not reported Not reported Not reported
RA‐ILD: 103 RA‐ILD: 60.9 RA‐ILD: 76.7
Correia et al, 2019 (58) Confirmed RA: 453 Confirmed RA: 59.6 White: 68.1% Confirmed RA: 80.6 Not reported Not reported Not reported Not reported
Unconfirmed RA: 1577 Unconfirmed RA: 55.7 African American: 14.2% Unconfirmed RA: 78.7
Hispanic: 1.8%
Asian: 2.0%
Darrah et al, 2018 (59) Total: 284 Anti‐PAD2 (−): 61 White (%) Anti‐PAD2 (−): 55 Ever, current Anti‐PAD2 (−): 8 y DAS28, HAQ, SHS, JSN Not reported
RA: 184, 55 had evidence of RA‐ ILD Anti‐PAD2 (+): 63 Anti‐PAD2 (−): 87 Anti‐PAD2 (+): 82 Anti‐PAD2 (−): 59, 12 Anti‐PAD2 (+): 9.5 y Anti‐PAD2 (−): 3.3, 0.75, 7, 5
Healthy controls: 100 Anti‐PAD2 (+): 82 Anti‐PAD2 (+): 62, 6 Anti‐PAD2 (+): 3.2, 1.0, 1, 8
Del Angel‐Pablo et al, 2020 (60) RA‐ILD: 65 RA‐ILD: 61 Study site: Mexico RA‐ILD: 53 RA‐ILD: 22 Age at RA diagnosis Not reported FVC% pred
RA without ILD: 82 RA without ILD: 53.50 RA without ILD: 81 RA without ILD: 19 RA‐ILD: 53 y RA‐ILD: 67
RA without ILD: 45.5 y RA without ILD: 97
Doyle et al, 2015 (31) BRASS, ACR BRASS, ACR Study site: USA BRASS, ACR BRASS, ACR Not reported Not reported BRASS, ACR
RA without ILD: 29, 22 RA without ILD: 53, 50 Clinical RA‐ILD: 76, 57 RA without ILD: 41, 42 FVC% pred
Subclinical RA‐ILD: 29, 18 Subclinical RA‐ILD: 68, 65 Subclinical RA‐ILD: 69, 44 Subclinical RA‐ILD: 80, 82
Clinical RA‐ILD: 17, 21 Clinical RA‐ILD: 65, 64 Clinical RA‐ILD: 53, 52 Clinical RA‐ILD: 70, 71
DLCO% pred
Subclinical RA‐ILD: 69, 61
Clinical RA‐ILD: 57, 53
England et al, 2019 (61) RA‐ILD: 90 RA‐ILD: 67.0 White: 76.7% Total: 9.9 Current: 26.1 RA‐ILD: 13.3 y DAS28 FVC% pred
RA without ILD: 1439 RA without ILD: 62.8 Former: 53.4 RA‐ILD: 4.1 RA‐ILD: 75.1
RA‐COPD: 294 RA‐COPD: 65.8 Never: 13.3
Fadda et al, 2018 (62) RA‐ILD: 6 RA‐ILD: 50 Study site: Egypt RA‐ILD: 87.3 Total: 1.1 RA‐ILD: 10.1 y CDAI by RA severity Not reported
RA without ILD: 25 RA without ILD: 48.4 RA without ILD: 80 RA without ILD: 10.4 y RA‐ILD: mild 6, moderate 16, severe 3
RA without ILD: mild 19, moderate 30, severe 14
Fotoh et al, 2021 (39) RA‐ILD: 75 RA‐ILD:47.4 Not reported RA‐ILD: 46.7 RA‐ILD: 85.3 RA‐ILD: 8.4 y Not reported FVC% pred
RA without ILD: 75 RA without ILD: 45.3 RA without ILD: 48 RA without ILD: 2.7 RA without ILD: 8.04 y RA‐ILD: 52.5
RA without ILD: 93.0
Fu et al, 2018 (63) Total: 92 RA without ILD: 61.5 Study site: China RA without ILD: 69.8 Not reported RA without ILD: 5 y DAS28 FVC% pred
RA without ILD: 43 RA without ILD: 5.6 Early ILD: 79.0
RA‐ILD: 49 Early ILD: 5.4 Established ILD: 81.5
Established ILD: 4.9
Fujita et al, 2020 (64) RA without ILD: 84 Total: 66 Study site: Japan Total: 71.6 Total: 37.9 Not reported Not reported Not reported
RA‐ILD: 31
Furukawa et al, 2012 (34) RA‐ILD: 129 RA‐ILD: 69.5 Japanese RA‐ILD: 67.4 RA‐ILD: 32.6 RA‐ILD: 17.1 y Steinbrocker stages III and IV (%) Not reported
RA without ILD: 321 RA without ILD: 61.7 RA without ILD: 85.4 RA without ILD: 27.4 RA without ILD: 13.5 y RA‐ILD: 65.1
RA without ILD: 55.1
Furukawa et al, 2013 (65) RA‐ILD: 26 RA‐ILD: 67.6 Study site: Japan RA‐ILD: 61.5 Not reported Not reported Not reported Not reported
RA without ILD: 38 RA without ILD: 59.7 RA without ILD: 84.2
Furukawa et al, 2020 (66) RA‐ILD: 100 RA‐ILD: 67.3 Study site: Japan RA‐ILD: 76 RA‐ILD: 38.1 Not reported Not reported Not reported
RA without ILD: 100 RA without ILD: 66.2 RA without ILD: 24 RA without ILD: 32.6
Giles et al, 2014 (67) RA‐ILD: 58 RA‐ILD: 61 White: 86% RA‐ILD: 50 RA‐ILD: 76 Total: 8 y DAS28 PFT restriction or impaired diffusion: 21%
RA without ILD: 118 RA without ILD: 58 RA without ILD: 64 RA without ILD: 53 RA‐ILD: 3.8
RA without ILD: 3.5
HAQ
RA‐ILD: 0.75
RA without ILD: 0.6
Giles et al, 2014 (68) RA‐ILD: 57 RA‐ILD: 61 White RA‐ILD: 51 Current, ever RA‐ILD: 9 y DAS28‐CRP PFT restriction or low DLCO, %
RA without ILD: 120 RA without ILD: 58 RA‐ILD: 88% RA without ILD: 64 RA‐ILD: 23, 75 RA without ILD: 8 y RA‐ILD: 3.8 RA‐ILD: 40
RA without ILD: 86% RA without ILD: 6, 53 RA without ILD: 3.5 RA without ILD: 13
Harlow et al, 2013 (69) RA‐ILD: 58 Not reported RA‐ILD RA‐ILD: 2.0 RA‐ILD Not reported DAS28 Not reported
RA without ILD: 27 citHSP90 (+): White 100% citHSP90 (+): 80 ever RA‐ILD
MCTD: 41 citHSP90 (−): White 88% citHSP90 (−): 91 ever citHSP90 (+): 3.8
IPF: 33 citHSP90 (−): 3.6
Hillarby et al, 1993 (70) RA‐ILD: 23 RA‐ILD: 50.3 Study site: UK RA‐ILD: 39 Not reported RA‐ILD: 16.7 y Not reported Not reported
RA without ILD: 153 RA without ILD: 45.2 RA without ILD: 73 RA without ILD: 9.8 y
Hussein et al, 2021 (71) RA‐ILD: 50 RA‐ILD: 67.3 Study site: Egypt RA‐ILD: 70 RA‐ILD: 10 RA‐ILD: 16.5 y DAS28 Cough/dyspnea (%)
RA without ILD: 50 RA without ILD: 55.8 RA without ILD: 94 RA without ILD: 6 RA without ILD: 10.5 y RA‐ILD: 4.89 RA‐ILD: 16/24
RA without ILD: 3.6
HAQ
RA‐ILD: 1.2
RA without ILD: 0.93
Juge et al, 2018 (32) RA‐ILD: 620 RA‐ILD: 69 Multinational RA‐ILD: 61.1 RA‐ILD: 54.7 Not reported Erosions (%) RA‐ILD
RA without ILD: 614 RA without ILD: 60.4 RA without ILD: 82.6 RA without ILD: 36.1 RA‐ILD: 46.5 FVC% pred: 78.2
Control: 5448 RA without ILD: 58.4 DLCO% pred: 57.6
Kass et al, 2019 (29) VA, non‐VA VA, non‐VA Study site: USA VA, non‐VA VA, non‐VA VA, non‐VA VA, non‐VA (DAS28) FVC% pred
Control: NA, 36 Control: NA, 65 Control: NA, 36 Control: NA, 52 RA without ILD: 10 y, 8 y RA without ILD: 4.1, 3.4 VA, non‐VA
IPF: NA, 100 IPF: NA, 70 IPF: NA, 33 IPF: NA, 68 RA‐ILD: 10 y, 13 y RA‐ILD: 3.7, 3.5 IPF: NA, 71
RA without ILD: 17, 22 RA without ILD: 57, 49 RA without ILD: 35, 76 RA without ILD: 41,42 RA without ILD: 84, 104
RA‐ILD: 86, 49 RA‐ILD: 64, 65 RA‐ILD: 5, 63 RA‐ILD: 88, 55 RA‐ILD: 83, 71
Kelly et al, 2014 (72) RA‐ILD: 230 RA‐ILD: 56 Study site: UK RA‐ILD: 52 Female, male RA‐ILD: 9 y Not reported Not reported
RA without ILD: 230 RA‐ILD: 60, 75
RA without ILD: 59, 60
Kim et al, 2020 (40) RA‐ILD: 84 Total: 61.4 Study site: Republic of Korea Total: 54.8 Total: 44 RA‐ILD: 47 mo Not reported FVC% pred
UIP: 30 UIP: 65.8 UIP: 46.7 UIP: 53.3 RA‐ILD, UIP: 66 mo Total: 74.0
RA‐ILD, non‐UIP: 37 mo UIP: 74.7
Non‐UIP: 73.6
Lai et al, 2019 (73) RA‐ILD: 100 RA‐ILD: 63.9 Study site: China RA‐ILD: 57 Not reported RA‐ILD: 9.87 y Not reported Not reported
RA without ILD: 100 RA without ILD: 53.3 RA without ILD: 75 RA without ILD: 9.05 y
Lee et al, 2016 (41) RA‐ILD: 62 Total: 64 Study site: South Korea Total: 48.4 Total: 51.6 Not reported Not reported Not reported
Lee et al, 2019 (45) RA‐ILD: 41 CTD‐ILD (+): 56.4 Study site: South Korea CTD‐ILD (+): 83.6 Not reported Not reported Not reported FVC% pred
RA without ILD: 106 CTD‐ILD (−): 51.1 CTD‐ILD (−): 88.5 CTD‐ILD (+): 77.2
SSc: 74
IM: 108
Other CTD: 220
Ma et al, 2019 (74) RA without ILD: 45 RA without ILD: 68 Study site: China RA without ILD: 80 RA without ILD: 17.8 RA without ILD: 6 y DAS28 RA‐ILD DLCO Impairment
RA‐ILD: 42 RA‐ILD: 58.1 RA‐ILD: 69 RA‐ILD: 23.8 RA‐ILD: 7 y RA without ILD: 4.9 Normal: 35%
Control: 49 RA‐ILD: 5.0 Slight: 38%
Moderate: 23%
Severe: 3.8%
Maniwa et al, 2000 (75) RA without ILD: 38 RA without ILD: 56 Study site: Japan RA without ILD: 73.7 Not reported Range 3 mo to 15 y RA without ILD, RA‐ILD DLCO% pred
RA‐ILD: 32 RA‐ILD: 63 RA‐ILD: 53.1 Lansbury Index: 55%, 46% RA without ILD: 78
Control: 40 Control: 50 Control: 37.5 RA‐ILD: 45
Matsuo et al, 2019 (76) RA‐ILD: 26 RA‐ILD: 69.8 Study site: Japan RA‐ILD: 80.8 Former, current RA‐ILD: 15.7 y DAS28 Not reported
RA without ILD: 286 RA without ILD: 62.9 RA without ILD: 87.4 RA‐ILD: 31, 0 RA without ILD: 14.8 y RA‐ILD: 2.8
RA without ILD: 30, 7 RA without ILD: 2.5
HAQ
RA‐ILD: 1.1
RA without ILD: 0.9
Matsushita et al, 2017 (77) Total: 228 Total: 62.9 Study site: Japan Total: 80.7 Not reported Not reported Not reported Not reported
RA‐ILD: 56
Mori et al, 2012 (78) RA‐ILD: 24 RA‐ILD: 72.5 Study site: Japan RA‐ILD: 50 RA‐ILD: 45.8 RA‐ILD: 1.5 y Steinbroker stages III and IV (%) Respiratory symptoms (%)
RA without ILD: 302 RA without ILD: 59.0 Race and ethnicity: not reported RA without ILD: 20.5 RA‐ILD: 41.7 RA‐ILD: 33.3
RA airway disease: 30 RA airway disease: 64.5 RA airway disease: 10 RA without ILD: 32.5 RA without ILD: 0
RA airway disease: 86.7 RA airway disease: 73.3
Nakajima et al, 2000 (79) SSc: 47 All RA: 61.8 Study site: Japan All RA: 70.1 Not reported All RA: 8.4 y Not reported Not reported
PM/DM: 21 SSc: 52.4 SSc: 87.2
SLE: 18 PM/DM: 48.5 PM/DM: 85.7
RA‐ILD: 22 SLE: 37.0 SLE: 83.3
RA without ILD: 35
SSc‐IP: 24
DM/PM‐IP: 14
SLE‐IP: 1
Natalini et al, 2021 (80) Total: 2328 Total: 64 White: 76.3% Total: 10.7 Never: 21.0 Total: 8.0 y Not reported Not reported
Prevalent RA‐ILD: 100 Black or African American: 14.8% Former: 25.1
Incident RA‐ILD: 83 Hispanic or Latino: 4.3% Current: 54.0
Newton et al, 2019 (37) IPF: 499 RA‐ILD: 60.2 RA‐ILD: 65% White RA‐ILD: 66 RA‐ILD: 65 Not reported Not reported FVC% pred
RA‐ILD: 62 IPF: 67
SSc‐ILD: 74 CTD‐ILD: 68
CTD‐ILD: 112 DLCO% pred
IPF: 47
CTD‐ILD: 53
ΔFVC% pred year−1
RA‐ILD: −0.59
SSc‐ILD: −1.03
Oka et al, 2016 (35) Total: 1383 UIP: 69.8 Study site: Japan UIP: 56.1 UIP: 44.2 Not reported Steinbroker stages III and IV (%) Not reported
UIP: 107 NSIP: 69.6 NSIP: 73.8 NSIP: 45.2 UIP: 59.3
NSIP: 183 BLAD: 68.2 BLAD: 84.3 BLAD: 38.8 NSIP: 51.6
BLAD: 116 BEAD: 66.8 BEAD: 85.8 BEAD: 38.2 BLAD: 64.9
BEAD: 121 Emphysema: 67.8 Emphysema: 36.1 Emphysema: 81.6 BEAD: 53.3
Emphysema: 83 CLD (−): 61.3 CLD (−): 84.8 CLD (−): 35.6 Emphysema: 40.4
CLD (−): 773 CLD (−): 48.6
Oka et al, 2017 (81) RA without ILD: 32 RA without ILD: 57.7 Study site: Japan RA without ILD: 93.7 RA without ILD: 32.3 RA without ILD: 10.6 y DAS28 Not reported
RA‐ILD: 32 RA‐ILD: 70.2 RA‐ILD: 34.4 RA‐ILD: 33.3 RA‐ILD: 15.5 y RA without ILD: 3.1
RA‐UIP: 70.6 RA‐UIP: 50 RA‐UIP: 38.9 RA‐UIP: 12.8 y RA‐ILD: 4.0
RA‐NSIP: 69.6 RA‐NSIP: 85.7 RA‐NSIP: 25.0 RA‐NSIP: 18.9 y RA‐UIP: 3.7
RA‐NSIP: 4.3
Pulito‐Cueto et al, 2020 (82) RA‐ILD: 20 RA‐ILD: 66.8 Study site: Spain RA‐ILD: 45 RA‐ILD: 65 RA‐ILD: 9.2 y Not reported FVC% pred
RA without ILD: 25 RA without ILD: 60.1 RA without ILD: 60 RA without ILD: 52 RA without ILD: 4.1 y RA‐ILD: 95.1
IPF: 21 IPF: 69.2 IPF: 33.3 IPF: 76.2 RA without ILD: 99.2
IPF: 84.9
Ren et al, 2021 (83) Total: 348 RA: 59.9 Study site: China RA: 74.4 All RA: 15.6 RA: 54 mo Not reported Not reported
RA‐ILD: 49
Restrepo et al, 2015 (84) RA without ILD: 563 RA without ILD: 52.9 RA‐ILD: 36% White RA‐ILD: 50.7 RA without ILD: 54.5 RA without ILD: 10.2 y DAS28 Not reported
RA‐ILD: 69 RA‐ILD: 60.2 RA‐ILD: 72.4 RA‐ILD: 12.6 y RA without ILD: 5.3
RA‐ILD: 6.0
Rocha‐Muñoz et al, 2015 (85) RA‐ILD: 39 RA‐ILD: 51 Study site: Mexico 100 RA‐ILD: 31 RA‐ILD: 7 y DAS28 FVC% pred
RA without ILD: 42 RA without ILD: 49 RA without ILD: 23.1 RA without ILD: 6.5 y RA‐ILD: 3.9 RA‐ILD: 71
RA without ILD: 2.5 RA without ILD: 86
Saku et al, 2021 (42) RA‐ILD: 72 RA‐ILD: 68.6 Study site: Japan RA‐ILD: 41.7 Ever: 69.5 Not reported Not reported FVC% pred
RA‐ILD: 83
Salaffi et al, 2019 (86) RA‐ILD: 29 RA‐ILD: 66.6 Study site: Italy Total: 70.3 RA‐ILD: 34.5 RA‐ILD: 7.5 y DAS28‐ESR FVC% pred, DLCO% pred
RA without ILD: 122 RA without ILD: 54.7 RA without ILD: 21.3 RA without ILD: 7.5 y RA‐ILD 5.8 RA‐ILD: 69.9, 64.3
RA without ILD: 5.7 RA without ILD: 90.8, 77.3
Sargin et al, 2018 (26) RA‐ILD: 43 RA‐ILD: 60.1 Study site: Turkey RA‐ILD: 69.8 RA‐ILD: 11.6 Not reported Not reported Not reported
RA without ILD: 40 RA without ILD: 58.5 RA without ILD: 82.5 RA without ILD: 10
Sargin et al, 2021 (87) Total: 113 RA without ILD: 57.6 Not reported RA without ILD: 80 RA without ILD: 12 Total: 34.9 mo Not reported Not reported
RA‐ILD: 62.1 RA‐ILD: 63.6 RA‐ILD: 14
Shen et al, 2019 (88) CTD‐ILD: 332 RA‐ILD: 60.6 Study site: China RA‐ILD: 75 RA‐ILD: 21 Not reported Not reported Symptoms (%)
RA‐ILD: 52 Exertional dyspnea: 26.9
Cough: 32.7
Sherin et al, 2019 (89) RA‐ILD: 40 RA‐ILD: 50.0 Study site: Egypt RA‐ILD: 80 RA‐ILD: 10 RA‐ILD: 7 y DAS28 FVC% pred
RA without ILD: 60 RA without ILD: 41.3 RA without ILD: 93.3 RA without ILD: 6.7 RA without ILD: 7 y RA‐ILD: 4.8 RA‐ILD: 82.0
RA without ILD: 4.2 RA without ILD: 93.6
Skare et al, 2011 (90) Total: 71 Total: 43.9 (at RA diagnosis) Study site: Brazil Total: 85.9 Total: 35.8 Total: 11.8 y Not reported Total (%)
Dyspnea: 18.4
Cough: 9.0
Sokai et al, 2016 (46) Total: 69 Total: 65.5 Study site: Japan Total: 60.8 Current, former Total: 12.5 y Not reported FVC% pred
RA‐ILD: 23 RA‐ILD: 62.5 RA‐ILD: 39.1 Total: 13, 41 RA‐ILD: 8.5 y Total: 99.9
RA‐ILD: 17, 52 RA‐ILD: 97.9
Solomon et al, 2020 (91) Total: 427 RA‐UIP: 64 Study site: USA RA‐UIP: 50 RA‐UIP: 62 Not reported Not reported FVC% pred
RA‐UIP: 40 RA‐UIP: 72
Wada et al, 2010 (92) Total: 74 RA‐BD: 67.5 Study site: Japan RA‐BD: 84.6 Not reported RA‐BD: 11.0 y DAS28 Not reported
RA‐BD: 26 RA‐ILD: 67.1 RA‐ILD: 64 RA‐ILD: 12.3 y RA‐BD: 3.3
RA‐ILD: 25 RA‐ILD: 2.7
Wang et al, 2016 (27) RA without ILD: 83 RA without ILD: 54.1 Study site: China RA‐ILD: 42.9 Never, former, current Not reported Not reported Not reported
RA‐ILD: 28 RA‐ILD: 63.6 RA without ILD: 72, 17, 10
RA‐ILD: 54, 25, 21
Wang et al, 2019 (93) RA‐ILD: 48 RA‐ILD: 52.8 Study site: China RA‐ILD: 50 Not reported Not reported Not reported Not reported
RA without ILD: 50 RA without ILD: 53.4 RA without ILD: 48
Wang et al, 2020 (33) Total: 96 Total: 55.5 Study site: China Total: 66.6 Total: 16.7 RA without ILD: 6.0 y Not reported FVC% pred
RA without ILD: 51 RA without ILD: 46.1 RA without ILD: 72.6 RA without ILD: 12.7 RA‐ILD: 7.2 y RA‐ILD: 79.9
RA‐ILD: 45 RA‐ILD: 59.5 RA‐ILD: 60 RA‐ILD: 20
Wang et al, 2021 (94) RA‐ILD: 31 RA‐ILD: 59.84, 60.5 Study site: China RA‐ILD: 68.4, 75RA without Not reported Not reported Not reported Warrick score
RA without ILD: 75 RA without ILD: 58.83, 56.49 ILD: 87.5, 82.9 RA‐ILD: 7.0
Wen et al, 2018 (95) RA‐ILD: 64 Total: 60.3 Study site: China Total: 76.7 Total: 20.3 Not reported DAS28 Not reported
RA without ILD: 56 Total: 5.1
Wu et al, 2020 (28) RA‐ILD: 58 RA‐ILD: 65.7 Study site: China RA‐ILD: 62.1 RA‐ILD: 25.9 RA‐ILD: 5 y Not reported FVC% pred
RA without ILD: 29 RA without ILD: 61.8 RA without ILD: 86.2 RA without ILD: 3.4 RA without ILD: 6 y sPD‐L1(+): 79.8
sPD‐L1(−): 95.3
Xiangyang et al, 2012 (96) RA‐ILD: 59 All RA: 51 Study site: USA All RA: 84.3 Not reported All RA: 47 y DAS28 Not reported
RA without ILD: 62 All RA: 6.7
Xue et al, 2021 (97) RA‐ILD: 35 RA‐ILD: 60.4 RA‐ILD: 95% Chinese Han Total: 66.67 Current RA‐ILD: 10.1 DAS28 Not reported
RA without ILD: 67 RA‐ILD: 51.4 RA‐ILD: 55 RA‐ILD: 5.9
Yang et al, 2019 (98) RA‐ILD: 77 RA‐ILD: 56.6 Study site: Japan RA‐ILD: 75.3 Former/current RA‐ILD: 11.5 Erosions (%) FVC% pred
RA without ILD: 231 RA without ILD: 57.1 RA without ILD: 75.3 RA‐ILD: 14.7/5.9 RA without ILD: 10.8 RA‐ILD: 33.3 RA‐ILD: 81.2
RA without ILD: 11.8/4.6 RA without ILD: 19.5
Yin et al, 2014 (99) RA without ILD: 214 RA without ILD: 49.5 Study site: China RA without ILD: 75.2 RA without ILD: 19.2 RA without ILD: 4.0 DAS28 Not reported
RA‐ILD: 71 RA‐ILD: 58.3 RA‐ILD: 70.4 RA‐ILD: 25.4 RA‐ILD: 9.0 RA without ILD: 5.5
RA‐ILD: 5.2
Yu et al, 2019 (36) RA‐ILD: 40 RA‐ILD >90% Chinese Han RA‐ILD: 52.5 RA‐ILD RA‐ILD RA‐ILD RA‐ILD
RA without ILD: 41 Wnt5a (−): 60.1 Wnt5a (−): 44.8 Wnt5a (−): 9.2 DAS28‐ESR Wnt5a (−)/Wnt5a (+) (%)
Wnt5a (+): 61.1 Wnt5a (+): 81.8 Wnt5a (+): 12.6 Wnt5a (−): 5.3 Cough: 93.1/90.9
Wnt5a (+): 5.5 Dyspnea: 89.7/72.7
Zhang et al, 2017 (100) RA‐ILD: 237 RA‐ILD: 57.6 Study site: China RA‐ILD: 63.7 RA‐ILD: 40.9 RA‐ILD: 2 Not reported Respiratory symptoms (%)
RA without ILD: 313 RA without ILD: 47.7 RA without ILD: 64.8 RA without ILD: 4.8 RA without ILD: 4 RA‐ILD: 41
RA without ILD: 4.7
Zheng et al, 2021 (25) RA without ILD: 26 RA without ILD: 56.6 Study site: China RA without ILD: 76.9 RA without ILD: 15.4 RA without ILD: 3 Not reported HRCT fibrosis score
RA‐ILD: 24 RA‐ILD: 62.9 RA‐ILD: 41.7 RA‐ILD: 50 RA‐ILD: 4.5 RA‐ILD: 14.6
CTD‐ILD: 14 CTD‐ILD: 53.1 CTD‐ILD: 64.3 CTD‐ILD: 21.4
Zhou et al, 2020 (101) RA‐ILD: 20 RA‐ILD: 57.7 Study site: China Total: 100 Not reported RA‐ILD: 8.1 DAS28 Not reported
RA without ILD: 20 RA without ILD: 52.6 RA without ILD: 8.4 RA‐ILD: 6.3
RA without ILD: 5.9
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Abbreviations: ACR, American College of Rheumatology; BD, bronchial disease; BEAD, bronchiectatic airway disease; BLAD, bronchiolitic airway disease; BRASS, Brigham and Women's Hospital Rheumatoid Arthritis Sequential Study; CDAI, clinical disease activity index; citHSP, citrullinated heat shock protein; CLD, chronic lung disease; COPD, chronic obstructive pulmonary disease; CTD, connective tissue disease; CRP, C‐reactive protein; DAS28, disease activity score in 28 joints; DLCO, diffusion capacity for carbon monoxide; DM, dermatomyositis; FVC, forced vital capacity; HAQ, health assessment questionnaire; HRCT, high‐resolution computed tomography; ILD, interstitial lung disease; IM, inflammatory myositis; IP, interstitial pneumonia; IPF, idiopathic pulmonary fibrosis; JSN, joint space narrowing; MCTD, mixed connective tissue disease; NA, not available; NSIP, nonspecific interstitial pneumonia; PAD, peptidyl arginine deiminase; PFT, pulmonary function test; PM, polymyositis; pred, predicted; RA, rheumatoid arthritis; SHS, Sharp van der Heijde score; SLE, systemic lupus erythematous; sPD‐L, soluble programmed death‐ligand; SS, Sjögren syndrome; SSc, systemic sclerosis; UIP, usual interstitial pneumonia; VA, Department of Veterans Affairs; y, year(s); year−1, per year.

a

Values reported for FVC% pred and DLCO% pred were inconsistent.

Peripheral biomarkers that differentiated RA‐ILD from RA without ILD

From the 70 included studies, we identified 104 unique biomarkers that were able to differentiate RA‐ILD from RA without ILD in at least one analysis. These included cytokines and chemokines (n = 25), autoantibodies (n = 21), genetic markers (n = 15), growth factors (n = 8), extracellular matrix proteins (n = 5), tumor markers (n = 5), lung epithelial or surfactant proteins (n = 2), and those classified as “other” (n = 23) (Table 3). Of these biomarkers, 56 were associated with the presence of RA‐ILD in at least one adjusted analysis, 14 were associated with RA‐ILD in two or more studies, and six were associated with RA‐ILD in two or more studies with at least one being adjusted (rheumatoid factor [RF], anti‐CCP, Krebs von den Lungen 6 [KL‐6], surfactant protein D [SP‐D], carbohydrate antigen 19‐9 [CA‐19‐9], and matrix metalloproteinase 7 [MMP‐7]). Twelve biomarkers had a negative association with RA‐ILD, including human leukocyte antigen (HLA)–DR4.

Table 3.

Summary of peripheral blood biomarkers associated with the presence of RA‐ILD

Biomarker category Biomarkers associated with RA‐ILD vs. RA without ILD
Autoantibodies Multiple studies: rheumatoid factor, a anti‐CCP, a anti‐CEP‐1
Single study: anti‐MAA, a anti‐IL‐1a, anti‐aaRS (anti‐PL‐7, anti‐PL‐12), anti‐PAD2, a , b anti‐PAD3/4XR, a anti‐CarP, a anti‐fibrinogen A, anti‐apolipoprotein E, anti‐cit‐hsp90, anti‐cit‐fibrinogen A, anti‐cit‐apolipoprotein A1, anti‐cit‐lipoprotein E, anti‐cit‐vimentin, anti‐cit‐histone 2B, anti‐cit‐filaggrin, anti‐cit‐biglycan, anti‐cit‐clusterin
Lung epithelial–related proteins Multiple studies: KL‐6, a SP‐D a
Tumor markers Multiple studies: CA‐125, CA‐19‐9 a
Single study: CA‐15‐3, sPD‐L1, a Gal‐9
Cytokines and chemokines Multiple studies: IL‐6, IL‐33
Single study: IL‐1α, a IL‐1β, a IL‐2, a IL‐7, a IL‐8, a IL‐9, a IL‐11, IL‐12p40, a IL‐12p70, a IL‐13, IL‐15, a IL‐18, a TNF‐α, a IFN‐α2, a LPS‐BP, CXCL10, a CXCL16, CCL18, a GRO‐1, a MCP‐3, a MIP‐1β, a eotaxin, a fractalkine a
Growth factors Single study: FGF‐2, a FLT‐3L, a GM‐CSF, a TGF‐α, a TGF‐β1, VEGF, a DKK‐1, CTGF
Extracellular matrix proteins Multiple studies: MMP‐7 a
Single study: MMP‐1, a MMP‐2, a MMP‐9, a MMP‐10 a
Genetic polymorphisms Multiple studies: HLA‐DR2, HLA‐DR4 b
Single study: shared epitope, b HLA‐DQ4, b HLA‐DQ6, HLA‐DRB1*14:06, HLA‐DRB1*15:02, HLA‐DRB1*16:02‐DQB1*05:02, MUC5B mutations, MUC5B rs35705950, a hsa‐miR‐214‐5p, a hsa‐miR‐7‐6p, a lncRNA (NR_002819, ENST00000603415, ENST00000560199 b )
Others Multiple studies: ESR, CRP
Single study: lysine, 25‐OH vitamin D, a , b Wnt5a, a GGT, LDH, a immunoglobulins, decanoic acid, a , b glycerol, a morpholine, a , b dyphylline, a octanoic acid, a , b fumaric acid, a , b N‐acetylgalactosamine‐1, a immature platelet fraction, endothelial progenitor cells, % and absolute CD3‐CD56+ NK cells, a % T cells, a , b % CD4+ T cells, a , b platelet/lymphocyte ratio, neutrophil/lymphocyte ratio
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Abbreviations: aaRS, aminoacyl–transfer RNA synthetase; CA, cancer or carbohydrate antigen; CarP, carbamylated protein; CCL, chemokine ligand; CCP, cyclic citrullinated peptide; CEP‐1, citrullinated alpha‐enolase peptide; cit, citrullinated; cit‐hsp, citrullinated heat shock protein; CRP, C‐reactive protein; CTGF, connective tissue growth factor; CXCL, CXC chemokine ligand; DKK, dickkopf; ESR, erythrocyte sedimentation rate; FGF, fibroblast growth factor; FLT‐3L, FMS‐like tyrosine kinase 3 ligand; Gal, galactin; GGT, γ‐glutamyl transferase; GM‐CSF, granulocyte macrophage colony‐stimulating factor; GRO, growth‐related oncogene; HLA, human leukocyte antigen; hsa‐miR, Homo sapiens microRNA; IFN, interferon; IL, interleukin; ILD, interstitial lung disease; KL, Krebs von den Lungen; LDH, lactate dehydrogenase; lncRNA, long noncoding RNA; LPS‐BP, lipopolysaccharide‐binding protein; MAA, malondialdehyde‐acetaldehyde adducts; MCP, monocyte chemoattractant protein; MIP, macrophage inflammatory protein; MMP, matrix metallopeptidase; MUC5B, mucin5B; NK, natural killer; NR, nucleotide result; OH, hydroxy; PAD, peptidyl arginine deiminase; PL‐7, anti‐threonyl‐tRNA synthetase; PL‐12, anti‐alanyl‐tRNA‐synthetase; RA, rheumatoid arthritis; SP‐D, lung epithelial–derived surfactant protein D; sPD‐L, soluble programmed death‐ligand; TGF, transforming growth factor; TNF, tumor necrosis factor; VEGF; vascular endothelial growth factor; XR, cross‐reactive.

a

Biomarkers were associated with RA‐ILD in at least one adjusted analysis.

b

Biomarkers were negatively associated with RA‐ILD in at least one study.

RF and anti‐CCP were the most frequently studied autoantibodies, with conflicting findings for both (Supplementary Table 1). Although several studies, including those with adjusted analyses, found these autoantibodies to be more prevalent or present in higher concentrations in RA‐ILD, a nearly equal number of others found no association. KL‐6 (a mucin‐like glycoprotein also referred to as MUC1) and SP‐D (a collectin expressed in pulmonary epithelia), together categorized as lung epithelial and surfactant protein biomarkers, were both associated with RA‐ILD in the majority of analyses. CA‐19‐9 and soluble programmed death ligand 1 (sPD‐L1) were the most promising tumor markers, with both having an association with RA‐ILD in a multivariable analysis, though only CA‐19‐9 was validated in another study (25, 26, 27, 28). Most cytokines and growth factors were evaluated in a single study using a broad multianalyte biomarker discovery approach in two independent cohorts, with modest consistency of findings for a specific analyte across the cohorts (29). MMP‐7, an extracellular matrix protein involved in lung tissue repair, was evaluated in three separate studies that each performed adjusted analyses in two independent cohorts. MMP‐7 concentration was associated with RA‐ILD in all three studies but was only validated in an independent cohort in one of the studies (29, 30, 31). The mucin 5B (MUC5B) promotor variant rs35705950 and other MUC5B mutations were the genetic variants most closely associated with RA‐ILD and were restricted to the UIP subset of RA‐ILD (32, 33). RA‐associated genes in the HLA family were not consistently more prevalent among patients with RA‐ILD; in fact, the shared epitope alleles, HLA‐DR4 and HLA‐DQ4, were negatively associated with RA‐ILD in at least one study (34, 35). Erythrocyte sedimentation rate (ESR) and C‐reactive protein (CRP), commonly assessed in the management of RA, had inconsistent findings in unadjusted analyses. Neither was associated with RA‐ILD in the single analysis with multivariable adjustment (36).

Peripheral biomarkers that differentiated RA‐ILD from a different type of lung disease

Fourteen biomarkers were found in at least one study to differentiate RA‐ILD from a different type of lung disease (Table 4). Comparator lung diseases included other RA‐associated lung diseases (eg, airway disease, bronchiectasis, and chronic obstructive pulmonary disease), other connective tissue disease–related ILDs (CTD‐ILDs), and idiopathic pulmonary fibrosis (IPF). None of the studies adjusted for potential confounders. The MUC5B promoter variant rs35705950 was more prevalent (minor allele frequency: RA‐ILD, 34.6%; systemic sclerosis–associated ILD [SSc‐ILD], 16.2%; CTD‐ILD, 12.7%) and leukocyte telomere length was shorter (observed‐minus‐expected telomere length: RA‐ILD, −0.14; SSc‐ILD, −0.02; CTD‐ILD, 0.00) in RA‐ILD compared with SSc‐ILD and a combined group of other CTD‐ILDs, although these analyses did not account for differences in ILD pattern (37). Among other biomarkers evaluated in multiple studies, RF and anti‐CCP did not differentiate RA‐ILD from other RA‐associated lung diseases. KL‐6 levels were higher in RA‐ILD compared with RA‐associated airway disease but did not differ between RA‐ILD and other CTD‐ILDs. ESR and CRP level were not different in RA‐ILD compared with other RA‐associated lung diseases but tended to be higher in RA‐ILD than in other CTD‐ILDs.

Table 4.

Peripheral blood biomarker identification of RA‐ILD vs. other lung disease

Biomarker (citation) Outcome
Autoantibodies
Anti‐cit‐hsp90 (69) Single unadjusted study found more frequent positivity for anti‐cit‐hsp90‐ß‐P (P = 0.01) and anti‐cit‐hsp90‐α (P = 0.04) but not anti‐cit‐hsp90‐β‐E (P = 0.07) in RA‐ILD compared with MCTD. Additionally, positivity for anti‐cit‐hsp90‐β‐P (P = 0.002) and anti‐cit‐hsp90‐β‐E (P = 0.006) but not anti‐cit‐hsp90‐α (P = 0.080) was more frequent in RA‐ILD compared with IPF.
Anti‐CCP antibody (46, 78, 91) Two unadjusted studies found no difference in anti‐CCP titers between RA‐ILD and RA airway disease (mean concentration/titer 222.8 [RA‐ILD] vs. 221.6 U/ml [RA airway disease], P = NS; LR 0.22, P = 0.64).
Another unadjusted study found IgA‐ACPA and/or IgG‐ACPA positivity in patients with RA‐UIP was more frequent than that in two IPF cohorts (P < 0.01 for all comparisons).
Anti‐MAA antibody (61) Single unadjusted analysis found higher concentrations of IgM anti‐MAA antibody in RA‐ILD compared with RA‐COPD (P = 0.01). There was no difference in concentrations of IgG anti‐MAA antibody (P = 0.09).
ANCAs (92) Single unadjusted study found no difference in titers of BPI‐ANCA (P = 0.09), cytoplasmic‐ANCA (P = 0.98), or perinuclear‐ANCA (P = 0.08) between RA‐ILD and RA bronchial diseases.
RF (46, 78, 92) Two studies found no difference in RF titer/concentration between RA‐ILD and RA airway disease (mean concentration/titer 208.3 [RA‐ILD] vs. 218.8 U/ml [RA airway disease], P = NS; LR 0.01, P = 0.93). Another found no difference in RF titers between RA‐ILD and RA bronchial diseases (mean concentration/titer 478 [RA‐ILD] vs. 190 IU/ml [RA bronchial disease]).
Lung epithelial–related proteins
KL‐6 (25,45,46) Three studies with unadjusted analyses, one finding higher levels of KL‐6 in RA‐ILD compared with RA airway disease (mean concentration/titer 646 [RA‐ILD] vs. 394.3 U/ml [RA airway disease], P = 0.018). Two other studies found no significant difference in KL‐6 levels between RA‐ILD and other CTD‐ILD (mean concentration/titer 558 [RA‐ILD] vs. 824.5 U/ml, P = 0.365; other effect size not reported, P = NS).
SP‐D (46) Single unadjusted study found no significant difference in SP‐D levels between RA‐ILD and RA airway disease (P = 0.081).
Tumor markers
Tumor markers (25, 55, 88) Three unadjusted studies. One found no difference in proportion positive for CEA, AFP, SCC, CA‐15‐3, CA‐125, CA‐19‐9, CA‐72‐4, or CYFRA‐21‐1 between RA‐ILD and IPF. Another found no difference in levels of CA‐19‐9, CA‐125, CEA, CA‐153, or CYFRA‐21‐1 between RA‐ILD and other CTD‐ILD. Another found greater levels of CA‐19‐9 in RA‐ILD compared with other CTD‐ILD (P < 0.001) but no difference in CA‐125 or CEA levels.
Genetic polymorphisms
C4 allotypes (70) Single unadjusted analysis found no difference in C4A or C4B null allele between RA‐ILD, RA bronchiectasis, RA nonsmokers without small airway obstruction, or RA nonsmokers with small airway obstruction.
HLA (70, 78) Two unadjusted studies. One found that HLA‐DRB*15:01 (P = 0.007) and 15:02 (P = 0.0005) were more likely in RA‐ILD vs. with RA airway disease. The other study evaluated numerous DQ and Dw alleles and found no difference between RA‐ILD, RA bronchiectasis, RA nonsmokers without small airway obstruction, and RA nonsmokers with small airway obstruction.
MUC5B rs35705950 promoter variant (37) Single Bonferroni‐corrected analysis found higher prevalence of MUC5B promoter variant in RA‐ILD compared with SSc‐ILD (MAF 34.6 [95% CI: 24.4‐46.3] vs. 16.6 [95% CI: 9.3‐26.6], P = 0.040) and other CTD‐ILD (MAF 12.7 [95% CI: 7.5‐20.4], P = 0.0015). ILD pattern was not accounted for in comparisons.
TOLLIP rs5743890 (37) Single Bonferroni‐corrected analysis found no difference in minor allele frequency between RA‐ILD, SSc‐ILD, and other CTD‐ILD (P = 0.072).
Others
Complement (88) Single unadjusted study found no difference in C3 or C4 levels between IPF and RA‐ILD.
CRP (25, 88, 92) Three unadjusted studies. One found higher CRP in RA‐ILD vs. other CTD‐ILD (mean concentration/titer 54.5 [RA‐ILD] vs. 11.7 mg/l [CTD‐ILD], P = 0.032). One found no difference in CRP between RA‐ILD and RA bronchial diseases (mean concentration/titer 2.2 [RA‐ILD] vs. 3.2 g/dl [RA bronchial disease], P = 0.82). Another found no difference in CRP between RA‐ILD and IPF (mean concentration/titer 31.64 [RA‐ILD] vs. 23.62 mg/dl [IPF], P = NS).
EPCs (82) Single unadjusted study found greater EPC frequency in IPF compared with RA‐ILD and RA‐UIP (P < 0.001 for both).
ESR (88, 92) Two unadjusted studies. One found higher ESR levels in RA‐ILD compared with IPF (mean concentration/titer 47.94 [RA‐ILD] vs. 27.39 mm/h [IPF], P < 0.05). Another found no difference in levels between RA‐ILD and RA bronchial diseases (P = 0.38) (mean concentration/titer 43.1 [RA‐ILD] vs. 55.2 mm/h [RA bronchial disease], P = NS).
Ig (88) Single unadjusted study found no difference in IgG, IgM, or IgA levels between IPF and RA‐ILD.
Leukocyte telomere length (37) Single Bonferroni‐corrected analysis found shorter leukocyte telomere length in RA‐ILD compared with SSc‐ILD (observed expected telomere length − 0.14 vs. −0.02, P = 0.013) and other CTD‐ILD (−0.014 vs. 0.00, P = 0.00042).
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Abbreviations: ACPA, anti–citrullinated protein antibodies; AFP, α‐fetoprotein; ANCA, antineutrophil cytoplasmic antibody; BPI‐ANCA, bactericidal/permeability–increasing antineutrophilic cytoplasmic antibody; CA, cancer or carbohydrate antigen; CCP, cyclic citrullinated peptide; CEA, carcinoembryonic antigen; CI, confidence interval; cit‐hsp, citrullinated heat shock protein; COPD, chronic obstructive pulmonary disease; CRP, C‐reactive protein; CTD, connective tissue disease; CYFRA, cytokeratin 19 fragments; EPC, endothelial progenitor cell; ESR, erythrocyte sedimentation rate; HLA, human leukocyte antigen; Ig, immunoglobulin; ILD, interstitial lung disease; IPF, interstitial pulmonary fibrosis; KL, Krebs von den Lungen; LR, likelihood ratio; MAA, malondialdehyde‐acetaldehyde adducts; MAF, minor allele frequency; MCTD, mixed connective tissue disease; ml, milliliter; MUC5B, mucin5B; NS, nonsignificant; RA, rheumatoid arthritis; RF, rheumatoid factor; SCC, squamous cell carcinoma antigen; SP‐D, lung epithelial–derived surfactant protein D; SSc, systemic sclerosis; TOLLIP, Toll‐interacting protein; U, unit; UIP, usual interstitial pneumonia.

Peripheral biomarkers for RA‐ILD prognostication

Eight studies (five case‐control studies and three retrospective cohort studies) evaluated the prognostic value of biomarkers in RA‐ILD (Table 5). Study outcomes varied and included mortality, disease progression by PFTs and/or imaging, and/or ILD exacerbations. Eight biomarkers were associated with a worse prognosis in at least one study. Six of these biomarkers were prognostic in adjusted analyses, and two were associated with poor outcomes in two or more studies. KL‐6 was the biomarker most consistently found to be associated with RA‐ILD outcome measures. In four of five studies, including one cohort study, KL‐6 was associated with increased mortality, a greater risk of ILD exacerbation, and/or more rapid disease progression (38, 39, 40, 41, 42). RF and anti‐CCP were evaluated in multiple studies and were not associated with mortality risk, with the exception of a single case‐control study performing unadjusted analysis of high‐titer RF (40). Similarly, multiple studies evaluating CRP and ESR did not consistently find these biomarkers to prognosticate poor outcomes. Other biomarkers, including MUC5B mutations and MMP‐7, were infrequently studied and did not appear highly prognostic.

Table 5.

Peripheral blood biomarker prognostication of RA‐ILD

Biomarker (citations) Outcome
Autoantibodies
Anti‐CCP (33, 40, 98) Three studies, all negative. One retrospective cohort study found high titer ACPA was associated with acute exacerbations and/or all‐cause mortality in univariate analysis (OR 3.949, 95% CI: 1.119‐13.932, P = 0.033) but did not persist after multivariable adjustment (OR 0.722, 95% CI: 0.262‐1.989, P = 0.528). Two other case‐control studies with unadjusted analyses found no difference in anti‐CCP positivity and/or titer between patients with RA‐ILD who died vs. survived.
RF (40, 42, 98) Three case‐control studies performed, with two negative and one positive. One found that over a 10‐year period, patients with RA‐ILD who died had a greater RF titer than those who survived (774.5 vs. 345.2 IU/mL, P = 0.001) but no difference in proportion RF‐positive (82.1% vs. 70.8%, P = 0.161). Multivariable logistic regression found no association between high RF titer (>3 times ULN) and mortality (OR 1.90, 95% CI: 0.56‐6.43). Another found no association between RF titer and mortality (HR 1.00, 95% CI: 0.999‐1.001, P = 0.31). Another of patients with RA‐ILD found that nonsurvivors had greater mean RF titer than survivors (349 vs. 86.1 IU/ml), P = 0.016), with no difference between groups in RF positivity (27 [nonsurvivors] vs. 39 [survivors], P = 0.673). In univariate analysis, RF >88 IU/ml was associated with mortality (HR 2.246, 95% CI: 1.066‐4.732, P = 0.033).
Lung epithelial–related proteins
KL‐6 (38‐42) Five studies, four positive, one negative. The only retrospective cohort design of the group was a study of RA‐UIP in which unadjusted analysis found an association with disease progression at 1 year (OR 1.001, 95% CI: 1.000‐1.002, P = 0.008) that was no longer significant after adjustment (OR 1.001, 95% CI: 1.000‐1.003, P = 0.077). KL‐6 levels were significantly greater during acute exacerbations compared with baseline disease (2147 vs. 794 U/ml, P < 0.001). Multivariate analysis revealed that high levels of KL‐6 (≥933 U/ml) were an independent predictor of mortality (HR 3.035, 95% CI: 1.168‐7.885, P = 0.023). The remaining four were case‐control designs. In one study of patients with RA‐ILD, KL‐6 was found to be a significant predictor of death, with an optimal cutoff of 685 U/ml (C‐index = 0.687, P = 0.004). High levels of KL‐6 (>685U/ml) were associated with mortality in multivariable analysis (HR 2.984, 95% CI: 1.227‐7.257, P = 0.016). When stratified by ILD pattern, the findings remained for the RA‐UIP group, but no association with mortality was found for the non‐UIP group. Another adjusted study found no association between KL‐6 and mortality (HR 1.003, 95% CI: 0.999‐1.006, P = 0.068). Other unadjusted studies found that baseline KL‐6 levels were significantly higher in those who experienced disease progression (1987 vs. 799 U/ml), P = 0.027) or did not survive a 1‐year follow‐up period (OR 1.016, 95% CI: 1.01‐1.02).
SP‐A (41) Single retrospective cohort study of RA‐UIP. Unadjusted analysis found no association with SP‐A levels and disease progression at 1 year (OR 1.004, P = 0.418). Levels were similar during acute exacerbations compared with baseline disease (P = 0.265).
SP‐D (38, 42) Two case‐control studies, both negative. In an adjusted analysis, there was no association with SP‐D and mortality (HR 1.001, P = 0.203). Another unadjusted analysis found no association between SP‐D concentrations and disease progression over a mean follow‐up of 3 years.
Genetic polymorphisms
MUC5B mutation (33) Single retrospective cohort study found that MUC5B mutations (rare [MAF <0.01] and deleterious variants not including rs35705950) were associated with acute exacerbations and/or all‐cause mortality in a univariate LR (OR 2.308 P = 0.043), though this was not statistically significant after multivariable adjustment (OR 2.312, 95% CI: 0.951‐5.620, P = 0.065).
Cytokines and chemokines
CCL18 (PARC) (38) Single case‐control study of RA‐ILD. Unadjusted analyses found no relationship between CCL18 concentrations and disease progression at mean follow‐up of 3 years. Data not reported.
IL‐32 (41) Single retrospective cohort study of RA‐UIP. Unadjusted analysis found no association with IL‐32 levels and disease progression at 1 year (OR 0.999, P = 0.218). Levels were no different during acute exacerbations compared with baseline disease (P = 0.461).
IL‐6 (41) Single retrospective cohort study of RA‐UIP. Adjusted analysis revealed an association with IL‐6 levels and disease progression at 1 year (OR 1.040, P = 0.039). IL‐6 levels tended to be higher during acute exacerbations, though not statistically significant (P = 0.068).
Extracellular matrix proteins
MMP‐7 (41) Single retrospective cohort study of RA‐UIP. Unadjusted analysis found no association between MMP‐7 levels and disease progression at 1 year (OR 1.099, P = 0.394). Levels were no different during acute exacerbations compared with baseline disease (P = 0.580).
Others
CRP (40, 41, 42, 97, 98) Five studies, with two positive and three negative. In a case‐control study of RA‐ILD, baseline CRP levels were higher in those who had died vs. those who had survived over a 10‐year period (37.2 vs. 25.1 mg/l, P = 0.009), though multivariable regression found no association with mortality (OR 1.34, 95% CI: 0.95‐1.88). In a retrospective cohort study, multivariable analysis found higher CRP concentrations were associated with fatal outcomes (OR 1.072, 95% CI: 1.000‐1.150, P = 0.049). An adjusted case‐control study found no association between CRP and mortality (HR 1.027, 95% CI: 0.954‐1.094, P = 0.458). In a retrospective cohort study of RA‐UIP, unadjusted analysis found higher CRP levels in patients whose disease progressed at 1 year compared with those whose disease did not progress (5.7 vs. 1.3 [units not reported], P = 0.013). Lastly, in a case‐control study of patients with RA‐ILD, there was no difference in mean CRP titers between survivors and nonsurvivors (3.9 [non‐survivors] vs. 2.7 mg/dl, P = 0.303), and univariate analysis found no association with mortality (HR 1.030, 95% CI: 0.972‐1.092, P = 0.322).
DKK‐1 (97) Single retrospective cohort study performed multivariable analysis, finding that DKK‐1 was associated with fatal outcomes (OR 15.764, 95% CI: 1.086‐228.843, P = 0.043). Median survival was longer for DKK‐1‐negative patients (5.1 vs. 2.7 years, P = 0.041).
ESR (41, 98) Two studies. In one case‐control study of RA‐ILD, baseline ESR was higher in those who had died vs. those who had survived over a 10‐year period (58 vs. 42.2 mm/h, P = 0.008), though multivariable analysis of mortality was null (OR 1.00, 95% CI: 0.97‐1.02). In another retrospective cohort study of RA‐UIP, unadjusted analysis found higher ESR in patients whose disease progressed at 1 year compared with those whose disease did not progress (74 vs. 41 mm/hour, P = 0.001).
Leukocyte indices (42) Single adjusted case‐control study found that higher monocyte count (HR 1.020, 95% CI: 1.004‐1.035, P = 0.018) and neutrophil count (HR 1.001, 95% CI: 1.001‐1.117, P = 0.026) were associated with mortality in RA‐ILD. No association was observed with lymphocyte count (HR 1.002, P = 0.385). Patients with high monocyte and neutrophil counts had worse survival than those with no (P < 0.001) or just one high lineage (P = 0.001).
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Abbreviations: ACPA, anti–citrullinated protein antibody; CCL, chemokine ligand; CCP, cyclic citrullinated peptide; CI, confidence interval; CRP, C‐reactive protein; DKK, dickkopf; ESR, erythrocyte sedimentation rate; HR, hazard ratio; IL, interleukin; ILD, interstitial lung disease; IU, international unit; KL, Krebs von den Lungen; LR, logistic regression; MAF, minor allele frequency; ml, milliliter; mm, millimeter; MMP, matrix metallopeptidase; MUC5B, mucin5B; OR, odds ratio; PARC, pulmonary and activation‐regulated chemokine; RA, rheumatoid arthritis; RF, rheumatoid factor; SP‐A, surfactant protein A; SP‐D, lung epithelial–derived surfactant protein D; U, unit; UIP, usual interstitial pneumonia; ULN, upper limit of normal.

Quality assessment

Study quality was highly variable, as assessed by the modified QUADAS‐2 or QUIPS (Supplementary Table 2). Among the nonprognostic studies, risk of bias was most frequently related to a lack of, or poorly described, exclusion criteria, occurring in 51 of the 66 studies. The reference standard used for determining the presence or absence of RA‐ILD introduced the potential for bias in 25 studies, typically for vaguely described diagnostic criteria or, rarely, for absence of cross‐sectional imaging in the diagnostic criteria. Statistical analyses were unadjusted in 41 studies, resulting in a high risk of confounding bias.

All eight of the prognostic studies had moderate risk of bias in the study participation domain because of low sample size and/or incompletely described source population and characteristics. Six of the eight studies were at moderate or high risk of confounding bias because of incompletely measuring or defining confounders or for lack of adjustment in statistical analyses.

DISCUSSION

This systematic review has identified several candidate peripheral blood biomarkers for RA‐ILD and has summarized their performance for distinguishing RA‐ILD from RA without ILD or from  other types of lung disease, as well as their ability to prognosticate RA‐ILD disease course. Biomarkers capable of serving these roles included cytokines and chemokines, genetic polymorphisms, autoantibodies, growth factors, extracellular matrix proteins, tumor markers, lung epithelial–related proteins, and others. Despite the large number of identified biomarkers and an increase in RA‐ILD biomarker research over the past decade, few biomarkers have been rigorously assessed with appropriate confounder adjustment and external validation. Thus, before adopting them for regular clinical use, these biomarkers need further evaluation in well‐designed studies and in additional RA‐ILD populations.

The most common study objective identified in our review was to evaluate biomarkers distinguishing RA‐ILD from RA without ILD. Such biomarkers could serve a valuable clinical role by identifying patients requiring further ILD evaluation (eg, PFTs, high‐resolution CT) and guiding discussions regarding treatment options and/or risk reduction (eg, smoking cessation). Biomarkers already widely used in RA diagnosis and management, including autoantibodies and acute phase reactants, were investigated in several studies. Across these studies, mixed findings were observed for RF, anti‐CCP, ESR, and CRP, with a general tendency for patients with RA‐ILD to be more often seropositive and have higher autoantibody and/or acute phase reactant levels. Hampering the clinical utility of these biomarkers is their limited specificity for RA‐ILD, as demonstrated by the discrepancy between frequency of seropositivity (60%‐80%) and the estimated prevalence of RA‐ILD (10%‐40%) (4, 5, 6, 7). In agreement with our findings, a recent meta‐analysis focused only on the role of RF and anti‐CCP antibody in the identification of RA‐ILD and found substantial heterogeneity across studies (43). Based on pooled data, authors of this report estimated higher odds of RA‐ILD related to RF and anti‐CCP positivity and titer, although only a minority of the studies included in this meta‐analysis performed multivariable adjustment. Moreover, findings varied based on geographic region, and there was the potential for publication bias. Based on these considerations, RF, anti‐CCP, ESR, and CRP do not appear to be sufficient biomarkers for RA‐ILD identification alone.

Beyond using existing RA‐related biomarkers, several efforts to test biomarkers have shown promise in IPF, a disease that shares histopathologic similarities to RA‐ILD in a UIP pattern (10, 13). KL‐6, SP‐D, and MMP‐7, which are lung epithelial and extracellular matrix proteins, were examined in multiple studies, including some with adjustment for potential confounders. The majority of these studies found an association between these biomarkers and RA‐ILD, but additional testing of these biomarkers is needed in larger populations to adequately define their clinical application. The MUC5B rs35705950 promoter variant, the strongest known genetic risk factor for IPF, was associated with RA‐ILD in a large multinational study (32, 44). This association was restricted to RA‐ILD in a UIP pattern resembling IPF, and the minor allele frequency was highly variable across regions. As biomarkers initially identified in IPF, they are clearly not specific to RA‐ILD. Rather, they appear to be indicators of ILD, regardless of etiology (eg, IPF, RA, other CTD). For example, KL‐6 levels differentiated between ILD and airway disease in RA but not between RA‐ILD and other CTD‐ILDs (25, 45, 46). In contrast, genetic variants associated with RA risk, such as shared epitope alleles, were not strongly associated with the presence of RA‐ILD. At present, it does not appear that a single biomarker has adequate specificity for both RA and ILD.

The disease course of RA‐ILD is variable and can be devastatingly progressive, with a median survival estimated between 3 and 8 years depending on the histopathologic and radiographic criteria used to characterize subtype and severity of disease (47, 48). The ability to risk stratify patients could have significant clinical implications because treatment options such as immunomodulatory therapies and antifibrotics may reduce morbidity and preserve lung function but carry significant risks (16, 49). However, few studies have evaluated the ability of biomarkers to predict the disease course. In a limited number of studies, a higher KL‐6 level was associated with mortality and RA‐ILD progression, though this was not universal. This observation parallels similar prognostic findings observed for KL‐6 in IPF and other CTD‐ILDs (50, 51). Ultimately, limited conclusions can be drawn from these studies given the risk of bias related to small sample sizes and likely confounding as well as variability in assessed measures of outcomes (eg, all‐cause mortality vs. PFT progression vs. acute exacerbations) and outcome timing. To advance toward a precision medicine approach of selecting therapies for patients with RA‐ILD that is likely to progress, larger prospective cohort studies with sufficient follow‐up, appropriate outcome assessments, and confounder adjustment are therefore required.

Although this systematic review focused on evaluating the performance of individual biomarkers, advances in multiplex biomarker assessment and big‐data analytics have opened the possibility of using composite biomarker profiles consisting of multiple individual biomarkers. Data reduction techniques can inform the creation of biomarker profiles that may improve diagnostic sensitivity and specificity over individual biomarkers alone (29). Indeed, some studies included in this review evaluated a large number of biomarkers not for their individual performance but rather to inform biomarker profile development. This consideration may explain the infrequency of adjustment for potential confounders in those analyses; however, critical appraisal of multibiomarker profiles for RA‐ILD identification and prognostication was beyond the scope of this review.

A limitation of this systematic review relates to the variability in study quality for reports identified in our search. Many did not adjust for potential confounders, and few biomarkers were assessed in multiple studies or externally validated. Moreover, the criteria for RA‐ILD, the composition of comparator groups, and the type and timing of prognostic outcomes were highly variable or inadequately detailed. Because of this variability, we could not perform meta‐analysis and were only able to narratively summarize findings. Identifying RA‐ILD biomarkers is an active area of expanding research productivity, and we were unable to include recent studies published after our search date. We focused on peripheral blood biomarkers because of their feasibility to obtain and be integrated into clinical care. Biomarkers collected from other sites (eg, bronchoalveolar lavage, sputum) may also be valuable in the assessment of RA‐ILD, and their appraisal will require future work. Although we used validated quality assessment tools, we did have to adapt them to ensure appropriate assessment for risk of bias related to confounding. Despite these limitations, this is among the first efforts to comprehensively and systematically assess and appraise the performance of peripheral blood biomarkers in RA‐ILD.

In conclusion, we have summarized the performance of peripheral blood biomarkers for identifying RA‐ILD and predicting disease outcomes. Autoantibodies, lung epithelial and surfactant proteins, cytokines, growth factors, extracellular matrix proteins, genetic markers, and various other biomarkers are candidates for filling these roles. Biomarkers identified in IPF (MMP‐7, SP‐D, and KL‐6) were those most closely associated with ILD among patients with RA in our review, but they appear to lack specificity for the underlying disease associated with ILD. All biomarkers considered require additional testing in larger well‐designed studies before they can be integrated into regular clinical care, and additional RA‐ILD biomarker discovery endeavors are encouraged.

AUTHOR CONTRIBUTIONS

All authors were involved in drafting the article or revising it critically for important intellectual content, and all authors approved the final version to be submitted for publication. Dr. England had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study conception and design

Van Kalsbeek, Shaver, Ebel, England.

Acquisition of data

Van Kalsbeek, Brooks, Shaver, Ebel, Schmidt, England.

Analysis and interpretation of data

Van Kalsbeek, Brooks, Ebel, Hershberger, Schmidt, Poole, Ascherman, Thiele, Mikuls, England.

Supporting information

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Appendix S1: Supplementary Information

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ACKNOWLEDGMENTS

We acknowledge support from the Leon S. McGoogan Health Sciences Library.

PROSPERO identifier: CRD42019137143.

The views expressed herein are those of the authors and do not necessarily represent the position or policy of the Department of Veterans Affairs or the US Government.

There was no funding directly supporting the conduct of this study. The authors disclose the following funding support: Dr. Poole's work was supported by the Department of Defense (PR200793) and the National Institute for Occupational Safety and Health (R01‐OH‐012045 and U54‐OH‐010162). Dr. Mikuls's work was supported by the VA Biomedical Laboratory Research and Development (I01 BX004660), the Department of Defense (PR200793), the Rheumatology Research Foundation, and the National Institute of General Medical Sciences (U54‐GM‐115458). Dr. England's work was supported by the VA Clinical Science Research and Development (IK2 CX002203), the Rheumatology Research Foundation, and the National Institute of General Medical Sciences (U54‐GM‐115458), which funds the Great Plains Institutional Development Award for Clinical and Translational Research Network.

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