Risk Factors Associated with Interstitial Lung Disease in Rheumatoid Arthritis: A Monocentric Clinical and Serological Assessment

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This study identified risk factors and developed a predictive model for RA-ILD in 208 RA patients from the Second Affiliated Hospital of Soochow University (2010–2023). ILD was confirmed via high-resolution computed tomography (HRCT). Logistic regression and ROC curve analyses determined optimal biomarker thresholds: rheumatoid factor (RF) > 352.5 IU/mL, anti-CCP antibodies > 43.25 IU/mL, complement C3 < 0.765 g/L, C4 1.7295 pg/mL. Univariate analysis linked male gender, smoking, elevated RF/anti-CCP, low C3/C4, high TNF-α, and reduced biologic therapy to ILD (all P < 0.05). Multivariate analysis confirmed C3, TNF-α, and biologic therapy as independent predictors ( P < 0.05). The nomogram demonstrated strong discrimination (C-index 0.829, 95% CI 0.756–0.902). RA-ILD exhibits distinct features (male predominance, smoking, dysregulated immunity), while biologic therapy may be protective. This model aids early risk stratification and clinical decision-making. Rheumatoid arthritis interstitial lung disease Risk Factor Cytokine Figures Figure 1 Figure 2 Introduction Rheumatoid arthritis (RA), a prevalent autoimmune disorder affecting 0.5–2% of the global population, involves a complex interplay of genetic, autoimmune, and environmental factors, driving synovial inflammation and systemic manifestations [ 1 – 3 ] . Up to 40% of RA patients develop extra-articular manifestations (EAMs), which worsen prognosis and increase mortality [ 2 , 4 – 9 ] . EAMs can occur at any disease stage, independent of joint severity or activity [ 10 ] . Interstitial lung disease (ILD), a severe EAM, causes progressive lung fibrosis and is the second-leading cause of RA-related death [ 11 – 13 ] . Diagnosis relies on HRCT, pulmonary function tests, and clinical assessment, but the lack of screening guidelines often leads to delayed detection [ 14 ] . Early prediction is critical to prevent irreversible damage. However, the incidence of RA-associated interstitial lung disease (RA-ILD) varies significantly among different studies, which depends on the heterogeneity of its clinical manifestations, disease progression, the applicability of diagnostic criteria, and the definition of disease types. There are currently no formal guidelines for the screening of RA-ILD, and the occurrence of RA-ILD runs through the course of RA, so many patients are often misdiagnosed, which affects their condition. Based on this, establishing a comprehensive and accurate prediction model for RA-ILD will help achieve early diagnosis and better treatment, thereby preventing more severe and irreversible lung damage [ 1 ] . Currently, numerous studies have explored various risk factors for RA-ILD, including demographic factors such as male sex, advanced age, RA disease duration, age at RA onset, and smoking status [ 13 – 15 ] . Research has also established associations between hematological markers and the risk of RA-ILD. C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR), which typically rise during active disease, have been suggested as potential biomarkers linked to both RA and RA-ILD [ 3 ] . RA is also characterized by the presence of various autoantibodies, most notably rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (anti-CCP antibody) [ 16 , 17 ] . Additionally, antinuclear antibody (ANA) have also been implicated in RA-ILD [ 18 , 19 ] . However, these markers often lack specificity, and the predictive power of any single indicator for RA-ILD remains limited. Therefore, developing a systematic approach to assess the risk of RA-ILD is an important area of research. The management of RA-ILD has progressed from conventional pharmacotherapy to more diversified strategies. However, the absence of specific treatment guidelines often results in suboptimal management of these severe extra-articular manifestations. This retrospective study aims to identify critical risk factors for RA-ILD and develop a comprehensive predictive model to enhance early diagnosis and risk stratification. Materials and Methods 1.1 Ethics Approval and Consent to Participate This study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University, Suzhou, China (Approval No.: JD-HG-2025-083). The Ethics Committee waived the requirement for informed consent for this study on the following grounds: the data used are anonymized with no identifiable information; the study poses minimal risk to participants; and obtaining consent for historical data is impracticable. Clinical trial number: not applicable. This study is a retrospective analysis and does not meet the definition of a clinical trial requiring registration. 1.2 Subjects This retrospective study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics Committee of the second hospital of Soochow university (Approval Number: LC202506200544). Patients who were treated at the Second Affiliated Hospital of Soochow University (General Hospital of Nuclear Industry) from January 1, 2010, to December 31, 2024. Patients diagnosed with RA according to the 2010 ACR/EULAR classification criteria for RA. All the enrolled RA patients underwent chest HRCT examination to determine whether ILD was present. Based on the HRCT results, RA patients were divided into two groups: RA-ILD group and RA non-ILD group. At the same time, these patients with other connective tissue diseases (CTD), infectious diseases, active hepatitis, human immunodeficiency diseases, malignancies and other lung diseases, including chronic obstructive pulmonary disease (COPD), bronchiectasis, pulmonary hypertension, tuberculosis, etc., were excluded. The information collected included basic patient data, past history, treatment regimen, and key hematological markers such as ANA, ESR, RF, anti-CCP antibodies, complement C3 and C4, and cytokines. Previous medical history included smoking history, hypertension, diabetes and other lung diseases, all of which were combined with the disease at the time of diagnosis. Body mass index (BMI) is calculated using the formula "BMI= weight (kg)/height 2 (m2)", and a BMI of 25 or greater is defined as overweight. According to the therapeutic drugs, the treatment plan is divided into plant drugs, non-steroidal anti-inflammatory drugs, hormone drugs and DMARDs. DMARDs are subdivided into traditional DMARDs and biologic DMARDs. 1.3 Cytokines in PBMCs Cytokines such as IL-2, IL-4, IL-6, IL-10, TNF alpha, and IFN - γ were identified by flow cytometry examination. 1.4 Hematological indicators Hematological indexes, including ESR, CRP, biochemical indexes, RF, Anti-CCP antibody, serum ferritin, etc., were collected. The reference gender for erythrocyte sedimentation rate (ESR) is defined as elevated in males with ESR>15mm/h and females with ESR>20mm/h. In addition, RF >20 IU/mL、Anti CCP antibody>5U/mL is defined as positive, and serum ferritin>150ng/mL is defined as elevated. 1.5 Statistical analysis We used SPSS 26.0 and R 3.6.0 statistical software for data analysis and graphical representation. Categorical data are presented as n (%) and analyzed using chi-square or Fisher's exact tests. Data are presented as median (Q1, Q3) and compared using Mann-Whitney U test for non-normally distributed variables. To determine optimal cutoff values for hematological indicators, we performed ROC curve analysis using the Youden index (J = sensitivity + specificity - 1) to identify optimal thresholds. Predictive performance was assessed by calculating the area under the curve (AUC), with values closer to 1 indicating better diagnostic accuracy for RA-ILD. To identify RA-ILD risk factors, we first performed univariate logistic regression on clinical variables. Significant variables (p<0.05) were then included in multivariate analysis to determine independent risk factors. Based on these results, we constructed a nomogram using the R "rms" package, with predictive performance assessed by the C-index. All analyses used two-sided tests with α=0.05. Results 2.1 General characteristics of patients This retrospective study included 208 patients with RA, consisting of 160 females and 48 males, with a median age of 63 years (interquartile range [IQR]: 55–73). The study cohort concluded 53 patients (25.5%) under 55 years old and 155 (74.5%) over 55 years old. In terms of weight status, 72.6% (n=151) of the patients were of normal weight, while 27.4% (n=57) were overweight. Regarding treatment patterns, the following were observed: conventional disease-modifying antirheumatic drugs (DMARDs) were used in 75.5% of cases, steroids in 65.9%, non-steroidal anti-inflammatory drugs (NSAIDs) in 40.4%, plant-based therapies in 38.9%, and biologics in 29.3%. Notably, 39 patients (18.8%) developed interstitial pneumonia, highlighting the significant burden of pulmonary complications in this patient population. A comparative analysis of clinical characteristics revealed significant disparities between RA-ILD and those without (RA non-ILD). The RA-ILD group had a significantly higher proportion of male patients (35.9%) than the RA non-ILD group (20.1%, P = 0.035). Similarly, smoking prevalence was markedly higher in RA-ILD patients (30.8%) versus non-ILD counterparts (11.8%; P = 0.003). No significant differences were observed between groups in age distribution, body mass status, alcohol consumption (all P > 0.05). Regarding pharmacological interventions, prior use of plant-based medications, NSAIDs, and corticosteroids did not differ significantly between RA-ILD and RA non-ILD groups (all P > 0.05). For disease-modifying antirheumatic drugs (DMARDs), traditional DMARD utilization showed no significant difference (χ² = 0.35, P = 0.553). However, RA-ILD patients had a significantly lower rate of prior biologic DMARD exposure (15.4%) than non-ILD patients (32.5%; χ² = 4.50, P = 0.034) (Table 1). 2.2 Immunological profiling between RA-ILD group and RA non ILD group Immunological profiling of 208 RA patients demonstrated distinct patterns between those with and without ILD. RF seropositivity was observed in 80.8% (168/208) of patients overall, with a trend toward higher prevalence in the RA-ILD group (89.7% [35/39] vs 78.7% [133/169]; χ²=2.49, p=0.115). Importantly, quantitative RF levels were significantly elevated in RA-ILD patients (median 826.4 IU/mL, IQR 52.7-1168.0) compared to non-ILD patients (median 254.5 IU/mL, IQR 28.3-246.3; p=0.001). Anti-CCP antibody analysis revealed significantly higher seropositivity in RA-ILD patients (94.9% [37/39] vs 79.9% [135/169]; χ²=4.98, p=0.026), despite comparable quantitative levels between groups (median 221.1 vs 251.3 IU/mL; p=0.141). No significant difference was observed in ANA positivity rates (56.4% [22/39] vs 56.8% [96/169]; χ²=0.002, p=0.964) ( Table 2,3). Complement analysis also revealed significant intergroup differences. 53 cases (25.5%) showed decreased C3 levels, with the RA-ILD group demonstrating a significantly higher prevalence of C3 reduction (48.7%, 19/39) compared to the non-ILD group (20.1%, 34/169; χ² = 13.65, P < 0.001; Table 2). Quantitative analysis further indicated that the median C3 level in the RA-ILD group was 0.81 (0.70-1.00) g/L, significantly lower than 0.92（0.81-1.10）g/L in the non-ILD group (P = 0.001; Table 3). Similarly, hypocomplementemia of C4 was also observed in 101 patients (48.6%), with the RA-ILD group having a higher proportion of patients with reduced C4 levels (71.8%, 28/39) than the non-ILD group (43.2%, 73/169; χ² = 10.38, P = 0.001; Table 2). The median C4 concentration also significantly differed between groups, with RA-ILD patients showing 0.17 g/L (IQR 0.15–0.25) compared to 0.23 g/L (IQR 0.16–0.26) in non-ILD patients (P = 0.001; Table 3). Elevated serum ferritin levels were observed in 47.1% (98/208) of patients, with no significant intergroup difference. Specifically, 43.6% (17/39) of RA-ILD patients exhibited elevated ferritin levels, compared to 47.9% (81/169) of non-ILD patients (χ² = 0.24, P = 0.625) (Table 2). Quantitative assessment similarly demonstrated comparable median ferritin levels between RA-ILD patients [250.8 ng/mL (IQR 127.0-287.5)] and non-ILD patients [232.1 ng/mL (IQR 88.0-327.8); p=0.301](Table 3). 2.3 Cytokine Profiling in RA Patients with and without ILD Flow cytometric analysis of circulating cytokines revealed distinct elevation patterns across our cohort. quantitative analysis showed no significant difference in IL-6 levels between RA-ILD [17.4 (1.1, 21.7) pg/mL] and non-ILD groups [30.1 (1.0, 37.5) pg/mL; P = 0.446; Table 3]. In contrast, TNF-α levels were significantly higher in the RA-ILD group [7.2 (1.9, 9.1) pg/mL] compared to the non-ILD group [2.6 (1.0, 3.1) pg/mL; P0.05; Table 3). 2.4 The predictive value of RF, anti CCP antibodies, and complement C3 and C4 for interstitial pneumonia Through ROC curve analysis, we evaluated the predictive value of RF, anti-CCP antibodies, complement components C3 and C4, and TNF-α for RA-ILD. The results demonstrated that TNF-α exhibited the strongest predictive performance (AUC=0.745, 95%CI: 0.664-0.773) with an optimal cutoff value of 1.7295 g/L. Complement components showed moderate predictive ability, with C4 (AUC=0.676) and C3 (AUC=0.674) having optimal cutoff values of 0.1935 g/L and 0.765 g/L, respectively. In contrast, RF (AUC=0.669) and anti-CCP antibodies (AUC=0.575) demonstrated relatively lower predictive value, with optimal cutoff values of 352.5 IU/mL and 43.25 IU/mL, respectively (Figure 1). Among these biomarkers, elevated TNF-α levels showed the strongest association with RA-ILD development. 2.5 Multi index construction of a column chart for rheumatoid arthritis complicated with interstitial pneumonia Given the limited predictive value of individual biomarkers for RA-ILD, we developed a comprehensive predictive model. This model incorporated demographic factors (gender and smoking history) that showed statistically significant differences in baseline characteristics, along with continuous measurements of serological markers (RF, anti-CCP antibodies, C3, C4, and TNF-α) and non-biological DMARD therapy . Using optimal cutoff values determined by ROC curve analysis, we categorized each parameter into high- and low-level groups(Figure 2). Univariate logistic regression analysis demonstrated significant associations between all examined variables and RA-ILD (all P<0.05, Table 4 ). Multivariate analysis further identified C3 levels, TNF-α concentrations, and non-biological DMARD therapy as independent predictive factors for RA-ILD (all P<0.05,Table 4). The predictive nomogram constructed based on these three independent factors exhibited good discriminative ability (C-index=0.829, 95% CI: 0.756-0.902, Figure 2). Discussion CTDs are chronic inflammatory disorders characterized by immune dysregulation, often involving multiple organ systems. Among these, ILD represents a common complication that can lead to significant pulmonary function impairment, reduced quality of life, and increased mortality. The prevalence of ILD varies markedly across different CTDs. For instance, ILD prevalence in systemic lupus erythematosus (SLE) is relatively low, whereas it is notably high in systemic sclerosis, reaching over 90% in some studies. By contrast, the reported prevalence of RA-ILD generally falls between these two extremes [20] . Our study documented an RA-ILD prevalence of approximately 18%, highlighting that this complication persists throughout the course of RA. Like other CTD-associated ILDs, RA-ILD is characterized by a poor prognosis, a finding consistent with the landmark study by Bongartz et al [21] . which demonstrated significantly worse outcomes in RA-ILD compared to RA without ILD. Recent studies have further validated this association, reporting markedly elevated 10-year mortality rates in RA-ILD patients (60.1% vs. 34.5% in non-ILD RA patients) [1] . Current evidence consistently identifies four primary causes of mortality in RA-ILD: acute exacerbations, progressive fibrotic ILD, lung cancer, and infectious pneumonia [22-25] . These findings underscore the critical need to develop dynamic and precise monitoring models for RA-ILD in clinical practice. Our study identified significant clinical characteristics in RA-ILD patients, including a marked male predominance that contrasts with some reports of higher female mortality [26, 27] . This gender paradox may reflect complex interactions between hormonal factors (e.g., reproductive history) and behavioral risks like smoking [28, 29] . Indeed, smoking prevalence was significantly higher in our RA-ILD cohort, consistent with established evidence linking smoking and COPD to RA-ILD development through pulmonary inflammation pathways [30, 31] . Emerging environmental risks, particularly PM 2.5 exposure with its ammonium components, appear to further potentiate ILD progression in RA [32-34] . Interestingly, while comorbidities like malignancies may influence RA-ILD risk, their associated immunosuppressive treatments might paradoxically offer protective effects [35] . Although RF remains a cornerstone diagnostic antibody for RA despite lacking disease specificity, our study demonstrated significantly elevated mean RF levels in RA-ILD patients compared to their non-ILD counterparts, despite comparable seropositivity rates. These findings corroborate cohort studies establishing RF as an independent predictor of RA-ILD [27] , with high-titer RF showing significant associations with poor prognostic indicators (mediastinal lymphadenopathy, honeycombing) and reduced transplant-free survival [36] . The relationship between RF/anti-CCP antibodies and ILD development remains contentious, with studies reporting both positive correlations [37] and null associations [38, 39] , likely reflecting population heterogeneity. Importantly, we identified complement C3 and TNF-α as novel predictive biomarkers, implicating humoral immunity in RA-ILD pathogenesis. These findings highlight the potential for targeted immunomodulatory approaches in RA-ILD management [39, 40] . Therapeutic analyses indicate that RA-ILD pathogenesis is closely associated with immune dysregulation, particularly abnormal activation of T/B cells and overproduction of cytokines such as TNF-α [41] . Correspondingly, our study found that biologic agents have protective effects against RA-ILD, consistent with their mechanism of targeted inhibition of specific inflammatory pathways [42, 43] . However, it must be emphasized that as a retrospective study, we cannot determine whether biologics prevent ILD in susceptible RA patients or modify established ILD lesions. These findings provide important directions for subsequent prospective studies, particularly the dual value of TNF-α as both a predictive biomarker and therapeutic target warrants in-depth exploration. Given the continuous nature and generally low positivity rates of hematological indicators, we established optimal predictive thresholds through ROC curve analysis. Using these refined cutoffs, we developed a prognostic nomogram incorporating three key independent predictors: C3 levels, TNF-α concentrations, and biologic agent use (adjusted for gender, smoking history, RF, anti-CCP antibodies, and C4 levels). The model demonstrated robust discriminative capacity (C-index 0.829, 95% CI 0.756-0.902). Our findings reveal distinct clinicopathological features characterizing RA-ILD, including: (1) demographic predisposition (male sex, smoking history); (2) elevated autoantibody titers (RF, anti-CCP); (3) complement consumption (reduced C3/C4); and (4) heightened TNF-α expression. Notably, biologic DMARDs may confer protection against ILD progression in this population. Study limitations include its single-center design and sample size constraints, necessitating future multicenter validation. Further refinement of disease activity metrics and treatment response assessments could enhance the precision of RA-ILD risk stratification models. Declarations Availability of data and materials The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Acknowledgments None. Author contributions Liuning Liu and Yabing Zhang wrote the manuscript and compiled supporting data，Ling Liu and Yabin Zhang contributed equally to the work.Wanggang and Lixinyue collected and analyzed research data, Zhichun Liu and Zhiqin Liu were responsible for the study design and manuscript revisions. The authors declare no competing interests related to this work. Funding This work was supported by Suzhou Medical and Health Science Technology Innovation Project (SKY2022146), Suzhou Science and Technology Bureau Clinical Trial Institution Capability Improvement Project (SLT2023035), and Jiangsu Provincial Health Key Research and Development Project (ZD2022032). References Akiyama M, Kaneko Y: Pathogenesis, clinical features, and treatment strategy for rheumatoid arthritis-associated interstitial lung disease. Autoimmunity reviews, 2022, 21(5):103056 . Smolen JS, Aletaha D, McInnes IB: Rheumatoid arthritis. Lancet, 2016, 388(10055):2023-2038 . Barile A, Arrigoni F, Bruno F, Guglielmi G, Zappia M, Reginelli A, Ruscitti P, Cipriani P, Giacomelli R, Brunese L . Computed Tomography and MR Imaging in Rheumatoid Arthritis. Radiologic clinics of North America 2017, 55(5):997-1007 . Ingegnoli F, Castelli R, Gualtierotti R. Rheumatoid factors: clinical applications. Disease markers 2013, 35(6):727-734 . Scott DL, Wolfe F, Huizinga TW.Rheumatoid arthritis. 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Anti-cyclic citrullinated peptide antibodies in lung diseases associated with rheumatoid arthritis. Clinical biochemistry, 2008, 41(13):1074-1077 . Zhou X, Li H, Wang N, Jin Y, He J. Respiratory infection risk in primary Sjögren's syndrome complicated with interstitial lung disease: a retrospective study. Clinical rheumatology 2024, 43(2):707-715 . Liu X, Zhang X, Shi J, Li S, Zhang X, Wang H: Serum biomarker-based risk model construction for primary Sjögren's syndrome with interstitial lung disease. Frontiers in molecular biosciences ,2024, 11:1448946 . Valenzi E, Tabib T, Papazoglou A, Sembrat J, Trejo Bittar HE, Rojas M, Lafyatis R. Disparate Interferon Signaling and Shared Aberrant Basaloid Cells in Single-Cell Profiling of Idiopathic Pulmonary Fibrosis and Systemic Sclerosis-Associated Interstitial Lung Disease. Frontiers in immunology, 2021, 12:595811 . Harrington R, Harkins P, Conway R. Targeted Therapy in Rheumatoid-Arthritis-Related Interstitial Lung Disease. J Clin Med. 2023;12(20):6657 . Tables Table 1. Basic Characteristics of the RA patients with or without interstitial pneumonia Variables(n/%) Total RA-ILD RA-non ILD χ2 P Value Gender Male 48（23.1） 14（35.9） 34（20.1） 4.44 0.035* Female 160（76.9） 25（64.1） 135（79.9） Age(year) ≤55 53（25.5） 8（20.5） 45（26.6） 0.062 0.430 ＞55 155（74.5） 31（79.5） 124（73.4） BMI ≥25 151（72.6） 27（69.2） 124（73.4） 0.27 0.601 ＜25 57（27.4） 12（30.8） 45（26.6） Smoking No 176（84.6） 27（69.2） 149（88.2） 8.73 0.003* Yes 32（15.4） 12（30.8） 20（11.8） Alcohol No 186(89.4) 34(87.2) 152(89.9) - 0.572 Yes 22(10.6) 5(12.8) 17(10.1) Plant drugs No 81（38.9） 16（41.0） 65（38.5） 0.09 0.767 Yes 127（61.1） 23（59.0） 104（61.5） NSAIDs Yes 84（40.4） 15（38.5） 69（40.8） 0.07 0.786 No 124（59.6） 24（61.5） 100（59.2） Steroids Yes 137（81.2） 27（69.2） 110（65.1） 0.24 0.623 No 71（18.8） 12（30.8） 59（34.9） csDMARDs Yes 157（75.5） 28 (71.8) 129（76.3） 0.35 0.533 No 51（24.5） 11（28.2） 40（23.7） bDMARDs Yes 61（29.3） 6（15.4） 55（32.5） 4.5 0.034 No 147（70.7） 33（84.6） 114（67.5） Non-steroidal anti-inflammatory drugs（NSAIDs）; conventional synthetic disease-modifying antirheumatic drugs(csDMARDs); biologic disease-modcifying anti-rheumatic drugs(bDMARDs)；# p<0.05. Table 2. Comparison of Positive Rates of serological markers between RA-ILD and Non-ILD Groups Positive rate χ2 P Total RA-ILD RA non-ILD RF 80.8% 89.7% 78.7% 2.49 0.115 Anti-CCP antibody 82.7% 94.9% 79.9% 4.98 0.026 # ANA 56.7% 56.4% 56.8% 0.01 0.964 ESR 70.4% 76.9% 73.4% 0.21 0.649 Serum ferritin 47.1% 43.6% 47.9% 0.24 0.625 Lower C3 25.5% 48.7% 20.1% 13.65 0.001 # Lower C4 48.6% 48.7% 20.1% 10.38 0.001 # IL6 52.4% 41.0% 55.0% 2.49 0.114 TNFα 13.9% 33.3% 9.4% 15.04 ＜0.001# The elevated thresholds were defined as follows: serum ferritin >150 ng/mL; rheumatoid factor (RF) >20 IU/mL and anti-cyclic citrullinated peptide (anti-CCP) antibody >5 U/mL were considered positive; erythrocyte sedimentation rate (ESR) >15 mm/h for males and >20 mm/h for females; Decreased complement was defined as C3 <0.70 g/L and C4 8.5 pg/mL for IL-6 and >3.5 pg/mL for TNF-α. # p<0.05. Table 3. Comparative Analysis of rheumatologic serological markers in RA-ILD versus Non-ILD Patients Variables Mean value P RA-ILD RA non-ILD RF (IU/ml) 355（52.7-1168） 82.6（28.3-246.3） 0.001# Anti-CCP Ab (IU/mL) 223（52.4-500） 125（18.3-500.0） 0.141 ESR 39.9±26.5 40.5±26.5 0.918 Serum ferritin 232.11±38.29 250.8±37.43 0.301 C3 0.81 (0.70-1.00) 0.92（0.81-1.10） 0.001# C4 0.17（0.15-0.25） 0.23（0.16-0.26） 0.001# # p<0.05. Table 4. Univariate logistic regression analysis of risk factors for interstitial lung disease in patients with rheumatoid arthritis. Variables Univariate analysis Multivariate analysis HR 95%CI P value OR 95%CI P value Male 2.224 1.045 – 4.729 0.0379* 1.051 0.353 – 3.132 0.9284 Smoking 3.311 1.451 – 7.554 0.0044* 2.723 0.825 – 8.983 0.1001 High RF titer 4.338 2.082 – 9.039 <0.001* 2.065 0.840 – 5.073 0.114 Lower anti-CCP Ab 0.368 0.153 – 0.883 0.0252* 0.580 0.200 – 1.680 0.3155 C3 5.179 2.413 – 11.112 <0.001* 4.000 1.426 – 11.223 0.0084* C4 3.875 1.808 – 8.307 0.001* 1.996 0.755 – 5.277 0.1636 TNFα 8.252 3.556 – 19.150 <0.001* 6.856 2.607 – 18.027 <0.001* BT 0.377 0.149 – 0.953 0.0392* 0.335 0.113 – 0.990 0.0479* BT（Biologic therapies） including TNF-α inhibitors, IL-6 inhibitors, or the CTLA-4 fusion protein abatacept . *p<0.05. Additional Declarations No competing interests reported. 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06:48:52","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":126032,"visible":true,"origin":"","legend":"","description":"","filename":"46dacd3d17c446aa996e92354d07e9f31structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7500736/v1/d0e0fc30f351b5f0e41e7277.xml"},{"id":92471166,"identity":"c995db59-e612-4068-a2f7-9a2b4228e354","added_by":"auto","created_at":"2025-09-30 06:48:52","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":132793,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7500736/v1/ecf59b580f20fb2c9f2a9806.html"},{"id":92471158,"identity":"f5d6f2a5-b04d-4400-ac26-afdcf224351d","added_by":"auto","created_at":"2025-09-30 06:48:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":499289,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Biomarkers for Interstitial Pneumonia in Rheumatoid Arthritis Patients.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7500736/v1/bd208beb2206cac88d50b9a5.png"},{"id":92471160,"identity":"aa85507b-3d27-4243-b0a5-580b27a9e9dd","added_by":"auto","created_at":"2025-09-30 06:48:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":166052,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the risk of RA-ILD based on multiple indicators.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7500736/v1/fe8ba12ec00c932b38598559.png"},{"id":94838656,"identity":"b74fe4c7-1019-4f3f-a18a-e48b622f5b44","added_by":"auto","created_at":"2025-10-31 08:54:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1443186,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7500736/v1/9487b469-4b4f-4d0b-a5ea-f9ca01543bd7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Risk Factors Associated with Interstitial Lung Disease in Rheumatoid Arthritis: A Monocentric Clinical and Serological Assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRheumatoid arthritis (RA), a prevalent autoimmune disorder affecting 0.5\u0026ndash;2% of the global population, involves a complex interplay of genetic, autoimmune, and environmental factors, driving synovial inflammation and systemic manifestations\u003csup\u003e[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Up to 40% of RA patients develop extra-articular manifestations (EAMs), which worsen prognosis and increase mortality\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5 CR6 CR7 CR8\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. EAMs can occur at any disease stage, independent of joint severity or activity\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eInterstitial lung disease (ILD), a severe EAM, causes progressive lung fibrosis and is the second-leading cause of RA-related death\u003csup\u003e[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Diagnosis relies on HRCT, pulmonary function tests, and clinical assessment, but the lack of screening guidelines often leads to delayed detection\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Early prediction is critical to prevent irreversible damage. However, the incidence of RA-associated interstitial lung disease (RA-ILD) varies significantly among different studies, which depends on the heterogeneity of its clinical manifestations, disease progression, the applicability of diagnostic criteria, and the definition of disease types. There are currently no formal guidelines for the screening of RA-ILD, and the occurrence of RA-ILD runs through the course of RA, so many patients are often misdiagnosed, which affects their condition. Based on this, establishing a comprehensive and accurate prediction model for RA-ILD will help achieve early diagnosis and better treatment, thereby preventing more severe and irreversible lung damage\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCurrently, numerous studies have explored various risk factors for RA-ILD, including demographic factors such as male sex, advanced age, RA disease duration, age at RA onset, and smoking status\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Research has also established associations between hematological markers and the risk of RA-ILD. C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR), which typically rise during active disease, have been suggested as potential biomarkers linked to both RA and RA-ILD\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. RA is also characterized by the presence of various autoantibodies, most notably rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (anti-CCP antibody)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Additionally, antinuclear antibody (ANA) have also been implicated in RA-ILD\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. However, these markers often lack specificity, and the predictive power of any single indicator for RA-ILD remains limited. Therefore, developing a systematic approach to assess the risk of RA-ILD is an important area of research.\u003c/p\u003e\u003cp\u003eThe management of RA-ILD has progressed from conventional pharmacotherapy to more diversified strategies. However, the absence of specific treatment guidelines often results in suboptimal management of these severe extra-articular manifestations. This retrospective study aims to identify critical risk factors for RA-ILD and develop a comprehensive predictive model to enhance early diagnosis and risk stratification.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.1 Ethics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the Second Affiliated Hospital of Soochow University, Suzhou, China (Approval No.: JD-HG-2025-083). The Ethics Committee waived the requirement for informed consent for this study on the following grounds: the data used are anonymized with no identifiable information; the study poses minimal risk to participants; and obtaining consent for historical data is impracticable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClinical trial number: not applicable. This study is a retrospective analysis and does not meet the definition of a clinical trial requiring registration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Subjects\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study was conducted in accordance with the principles of the Declaration of Helsinki and approved by the Ethics Committee of the second \u0026nbsp;hospital of Soochow university (Approval Number: LC202506200544). Patients who were treated at the Second Affiliated Hospital of Soochow University (General Hospital of Nuclear Industry) from January 1, 2010, to December 31, 2024. Patients diagnosed with RA according to the 2010 ACR/EULAR classification criteria for RA. All the enrolled RA patients underwent chest HRCT examination to determine whether ILD was present. Based on the HRCT results, RA patients were divided into two groups: RA-ILD group and RA non-ILD group. At the same time, these patients with other connective tissue diseases (CTD), infectious diseases, active hepatitis, human immunodeficiency diseases, malignancies and other lung diseases, including chronic obstructive pulmonary disease (COPD), bronchiectasis, pulmonary hypertension, tuberculosis, etc., were excluded.\u003c/p\u003e\n\u003cp\u003eThe information collected included basic patient data, past history, treatment regimen, and key hematological markers such as ANA, ESR, RF, anti-CCP antibodies, complement C3 and C4, and cytokines. Previous medical history included smoking history, hypertension, diabetes and other lung diseases, all of which were combined with the disease at the time of diagnosis. Body mass index (BMI) is calculated using the formula \u0026quot;BMI= weight (kg)/height 2 (m2)\u0026quot;, and a BMI of 25 or greater is defined as overweight. According to the therapeutic drugs, the treatment plan is divided into plant drugs, non-steroidal anti-inflammatory drugs, hormone drugs and DMARDs. DMARDs are subdivided into traditional DMARDs and biologic DMARDs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Cytokines in PBMCs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCytokines such as IL-2, IL-4, IL-6, IL-10, TNF alpha, and IFN -\u0026nbsp;\u0026gamma;\u0026nbsp;were identified by flow cytometry examination.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.4 Hematological indicators\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHematological indexes, including ESR, CRP, biochemical indexes, RF, Anti-CCP antibody, serum ferritin, etc., were collected. The reference gender for erythrocyte sedimentation rate (ESR) is defined as elevated in males with ESR\u0026gt;15mm/h and females with ESR\u0026gt;20mm/h. In addition, RF \u0026gt;20 IU/mL、Anti CCP antibody\u0026gt;5U/mL is defined as positive, and serum ferritin\u0026gt;150ng/mL is defined as elevated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.5 Statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used SPSS 26.0 and R 3.6.0 statistical software for data analysis and graphical representation. Categorical data are presented as n (%) and analyzed using chi-square or Fisher\u0026apos;s exact tests. Data are presented as median (Q1, Q3) and compared using Mann-Whitney U test for non-normally distributed variables. To determine optimal cutoff values for hematological indicators, we performed ROC curve analysis using the Youden index (J = sensitivity + specificity - 1) to identify optimal thresholds. Predictive performance was assessed by calculating the area under the curve (AUC), with values closer to 1 indicating better diagnostic accuracy for RA-ILD.\u003c/p\u003e\n\u003cp\u003eTo identify RA-ILD risk factors, we first performed univariate logistic regression on clinical variables. Significant variables (p\u0026lt;0.05) were then included in multivariate analysis to determine independent risk factors. Based on these results, we constructed a nomogram using the R \u0026quot;rms\u0026quot; package, with predictive performance assessed by the C-index. All analyses used two-sided tests with \u0026alpha;=0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1 General characteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study included 208 patients with RA, consisting of 160 females and 48 males, with a median age of 63 years (interquartile range [IQR]: 55\u0026ndash;73). The study cohort concluded 53 patients (25.5%) under 55 years old and 155 (74.5%) over 55 years old. In terms of weight status, 72.6% (n=151) of the patients were of normal weight, while 27.4% (n=57) were overweight. Regarding treatment patterns, the following were observed: conventional disease-modifying antirheumatic drugs (DMARDs) were used in 75.5% of cases, steroids in 65.9%, non-steroidal anti-inflammatory drugs (NSAIDs) in 40.4%, plant-based therapies in 38.9%, and biologics in 29.3%.\u003c/p\u003e\n\u003cp\u003eNotably, 39 patients (18.8%) developed interstitial pneumonia, highlighting the significant burden of pulmonary complications in this patient population. A comparative analysis of clinical characteristics revealed significant disparities between RA-ILD and those without (RA non-ILD). The RA-ILD group had a significantly higher proportion of male patients (35.9%) than the RA non-ILD group (20.1%, P = 0.035). Similarly, smoking prevalence was markedly higher in RA-ILD patients (30.8%) versus non-ILD counterparts (11.8%; P = 0.003).\u003c/p\u003e\n\u003cp\u003eNo significant differences were observed between groups in age distribution, body mass status, alcohol consumption (all P \u0026gt; 0.05). Regarding pharmacological interventions, prior use of plant-based medications, NSAIDs, and corticosteroids did not differ significantly between RA-ILD and RA non-ILD groups (all P \u0026gt; 0.05). For disease-modifying antirheumatic drugs (DMARDs), traditional DMARD utilization showed no significant difference (\u0026chi;\u0026sup2;\u0026nbsp;= 0.35, P = 0.553). However, RA-ILD patients had a significantly lower rate of prior biologic DMARD exposure (15.4%) than non-ILD patients (32.5%;\u0026nbsp;\u0026chi;\u0026sup2;\u0026nbsp;= 4.50, P = 0.034) (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Immunological profiling between RA-ILD group and RA non ILD group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Immunological profiling of 208 RA patients demonstrated distinct patterns between those with and without ILD. RF seropositivity was observed in 80.8% (168/208) of patients overall, with a trend toward higher prevalence in the RA-ILD group (89.7% [35/39] vs 78.7% [133/169]; \u0026chi;\u0026sup2;=2.49, p=0.115). Importantly, quantitative RF levels were significantly elevated in RA-ILD patients (median 826.4 IU/mL, IQR 52.7-1168.0) compared to non-ILD patients (median 254.5 IU/mL, IQR 28.3-246.3; p=0.001).\u003c/p\u003e\n\u003cp\u003eAnti-CCP antibody analysis revealed significantly higher seropositivity in RA-ILD patients (94.9% [37/39] vs 79.9% [135/169]; \u0026chi;\u0026sup2;=4.98, p=0.026), despite comparable quantitative levels between groups (median 221.1 vs 251.3 IU/mL; p=0.141). No significant difference was observed in ANA positivity rates (56.4% [22/39] vs 56.8% [96/169]; \u0026chi;\u0026sup2;=0.002, p=0.964) ( Table 2,3).\u003c/p\u003e\n\u003cp\u003eComplement analysis also revealed significant intergroup differences. 53 cases (25.5%) showed decreased C3 levels, with the RA-ILD group demonstrating a significantly higher prevalence of C3 reduction (48.7%, 19/39) compared to the non-ILD group (20.1%, 34/169; \u0026chi;\u0026sup2; = 13.65, P \u0026lt; 0.001; Table 2). Quantitative analysis further indicated that the median C3 level in the RA-ILD group was 0.81 (0.70-1.00) g/L, significantly lower than 0.92（0.81-1.10）g/L in the non-ILD group (P = 0.001; Table 3). Similarly, hypocomplementemia of C4 was also observed in 101 patients (48.6%), with the RA-ILD group having a higher proportion of patients with reduced C4 levels (71.8%, 28/39) than the non-ILD group (43.2%, 73/169; \u0026chi;\u0026sup2; = 10.38, P = 0.001; Table 2). The median C4 concentration also significantly differed between groups, with RA-ILD patients showing 0.17 g/L (IQR 0.15\u0026ndash;0.25) compared to 0.23 g/L (IQR 0.16\u0026ndash;0.26) in non-ILD patients (P = 0.001; Table 3).\u003c/p\u003e\n\u003cp\u003eElevated serum ferritin levels were observed in 47.1% (98/208) of patients, with no significant intergroup difference. Specifically, 43.6% (17/39) of RA-ILD patients exhibited elevated ferritin levels, compared to 47.9% (81/169) of non-ILD patients (\u0026chi;\u0026sup2; = 0.24, P = 0.625) (Table 2). Quantitative assessment similarly demonstrated comparable median ferritin levels between RA-ILD patients [250.8 ng/mL (IQR 127.0-287.5)] and non-ILD patients [232.1 ng/mL (IQR 88.0-327.8); p=0.301](Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Cytokine Profiling in RA Patients with and without ILD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow cytometric analysis of circulating cytokines revealed distinct elevation patterns across our cohort. quantitative analysis showed no significant difference in IL-6 levels between RA-ILD [17.4 (1.1, 21.7) pg/mL] and non-ILD groups [30.1 (1.0, 37.5) pg/mL; P = 0.446; Table 3]. In contrast, TNF-\u0026alpha; levels were significantly higher in the RA-ILD group [7.2 (1.9, 9.1) pg/mL] compared to the non-ILD group [2.6 (1.0, 3.1) pg/mL; P\u0026lt;0.001; Table 3]. No significant group differences were observed for IL-2, IL-4, IL-10, or IFN-\u0026gamma;\u0026nbsp;(all P\u0026gt;0.05; Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 The predictive value of RF, anti CCP antibodies, and complement C3 and C4 for interstitial pneumonia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough ROC curve analysis, we evaluated the predictive value of RF, anti-CCP antibodies, complement components C3 and C4, and TNF-\u0026alpha;\u0026nbsp;for RA-ILD. The results demonstrated that TNF-\u0026alpha;\u0026nbsp;exhibited the strongest predictive performance (AUC=0.745, 95%CI: 0.664-0.773) with an optimal cutoff value of 1.7295 g/L. Complement components showed moderate predictive ability, with C4 (AUC=0.676) and C3 (AUC=0.674) having optimal cutoff values of 0.1935 g/L and 0.765 g/L, respectively. In contrast, RF (AUC=0.669) and anti-CCP antibodies (AUC=0.575) demonstrated relatively lower predictive value, with optimal cutoff values of 352.5 IU/mL and 43.25 IU/mL, respectively (Figure 1). Among these biomarkers, elevated TNF-\u0026alpha;\u0026nbsp;levels showed the strongest association with RA-ILD development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Multi index construction of a column chart for rheumatoid arthritis complicated with interstitial pneumonia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the limited predictive value of individual biomarkers for RA-ILD, we developed a comprehensive predictive model. This model incorporated demographic factors (gender and smoking history) that showed statistically significant differences in baseline characteristics, along with continuous measurements of serological markers (RF, anti-CCP antibodies, C3, C4, and TNF-\u0026alpha;) and non-biological DMARD therapy . Using optimal cutoff values determined by ROC curve analysis, we categorized each parameter into high- and low-level groups(Figure 2).\u003c/p\u003e\n\u003cp\u003eUnivariate logistic regression analysis demonstrated significant associations between all examined variables and RA-ILD (all P\u0026lt;0.05, Table 4 ). Multivariate analysis further identified C3 levels, TNF-\u0026alpha; concentrations, and non-biological DMARD therapy as independent predictive factors for RA-ILD (all P\u0026lt;0.05,Table 4). The predictive nomogram constructed based on these three independent factors exhibited good discriminative ability (C-index=0.829, 95% CI: 0.756-0.902, Figure 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eCTDs are chronic inflammatory disorders characterized by immune dysregulation, often involving multiple organ systems. Among these, ILD represents a common complication that can lead to significant pulmonary function impairment, reduced quality of life, and increased mortality. The prevalence of ILD varies markedly across different CTDs. For instance, ILD prevalence in systemic lupus erythematosus (SLE) is relatively low, whereas it is notably high in systemic sclerosis, reaching over 90% in some studies. By contrast, the reported prevalence of RA-ILD generally falls between these two extremes\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[20]\u003c/sup\u003e. Our study documented an RA-ILD prevalence of approximately 18%, highlighting that this complication persists throughout the course of RA.\u003c/p\u003e\n\u003cp\u003eLike other CTD-associated ILDs, RA-ILD is characterized by a poor prognosis, a finding consistent with the landmark study by Bongartz et al\u003csup\u003e[21]\u003c/sup\u003e. which demonstrated significantly worse outcomes in RA-ILD compared to RA without ILD. Recent studies have further validated this association, reporting markedly elevated 10-year mortality rates in RA-ILD patients (60.1% vs. 34.5% in non-ILD RA patients)\u003csup\u003e[1]\u003c/sup\u003e. Current evidence consistently identifies four primary causes of mortality in RA-ILD: acute exacerbations, progressive fibrotic ILD, lung cancer, and infectious pneumonia\u0026nbsp;\u003csup\u003e[22-25]\u003c/sup\u003e. These findings underscore the critical need to develop dynamic and precise monitoring models for RA-ILD in clinical practice.\u003c/p\u003e\n\u003cp\u003eOur study identified significant clinical characteristics in RA-ILD patients, including a marked male predominance that contrasts with some reports of higher female mortality\u0026nbsp;\u003csup\u003e[26, 27]\u003c/sup\u003e. This gender paradox may reflect complex interactions between hormonal factors (e.g., reproductive history) and behavioral risks like smoking\u003csup\u003e[28, 29]\u003c/sup\u003e. Indeed, smoking prevalence was significantly higher in our RA-ILD cohort, consistent with established evidence linking smoking and COPD to RA-ILD development through pulmonary inflammation pathways\u003csup\u003e[30, 31]\u003c/sup\u003e. Emerging environmental risks, particularly PM 2.5 exposure with its ammonium components, appear to further potentiate ILD progression in RA\u003csup\u003e[32-34]\u003c/sup\u003e. Interestingly, while comorbidities like malignancies may influence RA-ILD risk, their associated immunosuppressive treatments might paradoxically offer protective effects\u003csup\u003e[35]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAlthough RF remains a cornerstone diagnostic antibody for RA despite lacking disease specificity, our study demonstrated significantly elevated mean RF levels in RA-ILD patients compared to their non-ILD counterparts, despite comparable seropositivity rates. These findings corroborate cohort studies establishing RF as an independent predictor of RA-ILD\u003csup\u003e[27]\u003c/sup\u003e, with high-titer RF showing significant associations with poor prognostic indicators (mediastinal lymphadenopathy, honeycombing) and reduced transplant-free survival\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[36]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe relationship between RF/anti-CCP antibodies and ILD development remains contentious, with studies reporting both positive correlations\u0026nbsp;\u003csup\u003e[37]\u003c/sup\u003e and null associations\u0026nbsp;\u003csup\u003e[38, 39]\u003c/sup\u003e, likely reflecting population heterogeneity. Importantly, we identified complement C3 and TNF-\u0026alpha; as novel predictive biomarkers, implicating humoral immunity in RA-ILD pathogenesis. These findings highlight the potential for targeted immunomodulatory approaches in RA-ILD management\u003csup\u003e[39, 40]\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTherapeutic analyses indicate that RA-ILD pathogenesis is closely associated with immune dysregulation, particularly abnormal activation of T/B cells and overproduction of cytokines such as TNF-\u0026alpha;\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e[41]\u003c/sup\u003e. Correspondingly, our study found that biologic agents have protective effects against RA-ILD, consistent with their mechanism of targeted inhibition of specific inflammatory pathways\u003csup\u003e[42, 43]\u003c/sup\u003e. However, it must be emphasized that as a retrospective study, we cannot determine whether biologics prevent ILD in susceptible RA patients or modify established ILD lesions. These findings provide important directions for subsequent prospective studies, particularly the dual value of TNF-\u0026alpha; as both a predictive biomarker and therapeutic target warrants in-depth exploration.\u003c/p\u003e\n\u003cp\u003eGiven the continuous nature and generally low positivity rates of hematological indicators, we established optimal predictive thresholds through ROC curve analysis. Using these refined cutoffs, we developed a prognostic nomogram incorporating three key independent predictors: C3 levels, TNF-\u0026alpha; concentrations, and biologic agent use (adjusted for gender, smoking history, RF, anti-CCP antibodies, and C4 levels). The model demonstrated robust discriminative capacity (C-index 0.829, 95% CI 0.756-0.902).\u003c/p\u003e\n\u003cp\u003eOur findings reveal distinct clinicopathological features characterizing RA-ILD, including: (1) demographic predisposition (male sex, smoking history); (2) elevated autoantibody titers (RF, anti-CCP); (3) complement consumption (reduced C3/C4); and (4) heightened TNF-\u0026alpha; expression. Notably, biologic DMARDs may confer protection against ILD progression in this population.\u003c/p\u003e\n\u003cp\u003eStudy limitations include its single-center design and sample size constraints, necessitating future multicenter validation. Further refinement of disease activity metrics and treatment response assessments could enhance the precision of RA-ILD risk stratification models.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLiuning Liu and Yabing Zhang wrote the manuscript and compiled supporting data，Ling Liu and Yabin Zhang contributed equally to the work.Wanggang and Lixinyue collected and analyzed research data, Zhichun Liu and Zhiqin Liu were responsible for the study design and manuscript revisions. The authors declare no competing interests related to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Suzhou Medical and Health Science Technology Innovation Project (SKY2022146), Suzhou Science and Technology Bureau Clinical Trial Institution Capability Improvement Project (SLT2023035), and Jiangsu Provincial Health Key Research and Development Project (ZD2022032).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAkiyama M, Kaneko Y: Pathogenesis, clinical features, and treatment strategy for rheumatoid arthritis-associated interstitial lung disease. Autoimmunity reviews, 2022, 21(5):103056 .\u003c/li\u003e\n\u003cli\u003eSmolen JS, Aletaha D, McInnes IB: Rheumatoid arthritis. Lancet, 2016, 388(10055):2023-2038 .\u003c/li\u003e\n\u003cli\u003eBarile A, Arrigoni F, Bruno F, Guglielmi G, Zappia M, Reginelli A, Ruscitti P, Cipriani P, Giacomelli R, Brunese L . 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Arthritis research \u0026amp; therapy ,2015, 17:319 .\u003c/li\u003e\n\u003cli\u003eSunyer J. Urban air pollution and chronic obstructive pulmonary disease: a review. The European respiratory journal, 2001, 17(5):1024-1033 .\u003c/li\u003e\n\u003cli\u003eFarhat SC, Silva CA, Orione MA, Campos LM, Sallum AM, Braga AL. Air pollution in autoimmune rheumatic diseases: a review. Autoimmunity reviews, 2011, 11(1):14-21 .\u003c/li\u003e\n\u003cli\u003eZhao N, Al-Aly Z, Zheng B, van Donkelaar A, Martin RV, Pineau CA, Bernatsky S. Fine particulate matter components and interstitial lung disease in rheumatoid arthritis. Eur Respir J. 2022l 28;60(1):2102149 .\u003c/li\u003e\n\u003cli\u003eKiely P, Busby AD, Nikiphorou E, Sullivan K, Walsh DA, Creamer P, Dixey J, Young A. Is incident rheumatoid arthritis interstitial lung disease associated with methotrexate treatment? Results from a multivariate analysis in the ERAS and ERAN inception cohorts. BMJ Open. 2019 ;9(5):e028466 .\u003c/li\u003e\n\u003cli\u003eGuo L, Wang J, Li J, Yao J, Zhao H. Biomarkers of rheumatoid arthritis-associated interstitial lung disease: a systematic review and meta-analysis. Front Immunol. 2024;15:1455346 .\u003c/li\u003e\n\u003cli\u003eKelly CA, Saravanan V, Nisar M, Arthanari S, Woodhead FA, Price-Forbes AN, Dawson J, Sathi N, Ahmad Y, Koduri G, Young A; British Rheumatoid Interstitial Lung (BRILL) Network. Rheumatoid arthritis-related interstitial lung disease: associations, prognostic factors and physiological and radiological characteristics--a large multicentre UK study. Rheumatology (Oxford). 2014;53(9):1676-82 .\u003c/li\u003e\n\u003cli\u003eChen J, Shi Y, Wang X, Huang H, Ascherman D. Asymptomatic preclinical rheumatoid arthritis-associated interstitial lung disease. Clinical \u0026amp; developmental immunology, 2013, 2013:406927 .\u003c/li\u003e\n\u003cli\u003eInui N, Enomoto N, Suda T, Kageyama Y, Watanabe H, Chida K. Anti-cyclic citrullinated peptide antibodies in lung diseases associated with rheumatoid arthritis. Clinical biochemistry, 2008, 41(13):1074-1077 .\u003c/li\u003e\n\u003cli\u003eZhou X, Li H, Wang N, Jin Y, He J. Respiratory infection risk in primary Sj\u0026ouml;gren\u0026apos;s syndrome complicated with interstitial lung disease: a retrospective study. Clinical rheumatology 2024, 43(2):707-715 .\u003c/li\u003e\n\u003cli\u003eLiu X, Zhang X, Shi J, Li S, Zhang X, Wang H: Serum biomarker-based risk model construction for primary Sj\u0026ouml;gren\u0026apos;s syndrome with interstitial lung disease. Frontiers in molecular biosciences ,2024, 11:1448946 .\u003c/li\u003e\n\u003cli\u003eValenzi E, Tabib T, Papazoglou A, Sembrat J, Trejo Bittar HE, Rojas M, Lafyatis R. Disparate Interferon Signaling and Shared Aberrant Basaloid Cells in Single-Cell Profiling of Idiopathic Pulmonary Fibrosis and Systemic Sclerosis-Associated Interstitial Lung Disease. Frontiers in immunology, 2021, 12:595811 .\u003c/li\u003e\n\u003cli\u003eHarrington R, Harkins P, Conway R. Targeted Therapy in Rheumatoid-Arthritis-Related Interstitial Lung Disease. J Clin Med. 2023;12(20):6657 .\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Basic Characteristics of the RA patients with or without interstitial pneumonia\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eVariables(n/%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003eRA-ILD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eRA-non ILD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026chi;2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003eP Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e48（23.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e14（35.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e34（20.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.035*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e160（76.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e25（64.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e135（79.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eAge(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026le;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e53（25.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e8（20.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e45（26.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e＞55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e155（74.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e31（79.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e124（73.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eBMI\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026ge;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e151（72.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e27（69.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e124（73.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e＜25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e57（27.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e12（30.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e45（26.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e176（84.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e27（69.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e149（88.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e8.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e32（15.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e12（30.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e20（11.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eAlcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e186(89.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e34(87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e152(89.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.572\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e22(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e5(12.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e17(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003ePlant drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e81（38.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e16（41.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e65（38.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e127（61.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e23（59.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e104（61.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eNSAIDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e84（40.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e15（38.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e69（40.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e124（59.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e24（61.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e100（59.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003eSteroids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e137（81.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e27（69.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e110（65.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e71（18.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e12（30.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e59（34.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003ecsDMARDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e157（75.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e28 (71.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e129（76.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e51（24.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e11（28.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e40（23.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 88px;\"\u003e\n \u003cp\u003ebDMARDs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e61（29.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e6（15.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e55（32.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 53px;\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e147（70.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e33（84.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e114（67.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNon-steroidal anti-inflammatory drugs（NSAIDs）; conventional synthetic disease-modifying antirheumatic drugs(csDMARDs); biologic disease-modcifying anti-rheumatic drugs(bDMARDs)；# p\u0026lt;0.05.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. Comparison of Positive Rates of serological markers between RA-ILD and Non-ILD Groups\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 51px;\"\u003e\n \u003cp\u003ePositive rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 15px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003eRA-ILD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eRA non-ILD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e80.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e89.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e78.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eAnti-CCP antibody\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e82.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e94.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e79.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e4.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.026\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eANA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e56.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e56.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e56.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.964\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e70.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e76.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e73.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.649\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eSerum ferritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e47.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e43.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e47.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.625\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eLower C3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e25.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e48.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e20.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e13.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eLower C4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e48.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e48.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e20.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e10.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.001\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eIL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e52.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e41.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e55.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e2.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eTNF\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e13.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e33.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003e9.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e＜0.001#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe elevated thresholds were defined as follows: serum ferritin \u0026gt;150 ng/mL; rheumatoid factor (RF) \u0026gt;20 IU/mL and anti-cyclic citrullinated peptide (anti-CCP) antibody \u0026gt;5 U/mL were considered positive; erythrocyte sedimentation rate (ESR) \u0026gt;15 mm/h for males and \u0026gt;20 mm/h for females; Decreased complement was defined as C3 \u0026lt;0.70 g/L and C4 \u0026lt;0.10 g/L; and cytokine levels \u0026gt;8.5 pg/mL for IL-6 and \u0026gt;3.5 pg/mL for TNF-\u0026alpha;. \u003csup\u003e#\u003c/sup\u003e p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 3. Comparative Analysis of rheumatologic serological markers in RA-ILD versus Non-ILD Patients\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003eMean value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003eRA-ILD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003eRA non-ILD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eRF (IU/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e355（52.7-1168）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e82.6（28.3-246.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.001#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eAnti-CCP Ab (IU/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e223（52.4-500）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e125（18.3-500.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eESR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e39.9\u0026plusmn;26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e40.5\u0026plusmn;26.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eSerum ferritin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e232.11\u0026plusmn;38.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e250.8\u0026plusmn;37.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.81 (0.70-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e0.92（0.81-1.10）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.001#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 25px;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e0.17（0.15-0.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27px;\"\u003e\n \u003cp\u003e0.23（0.16-0.26）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e0.001#\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e# p\u0026lt;0.05.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 4. Univariate logistic regression analysis of risk factors for interstitial lung disease in patients with rheumatoid arthritis.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 40px;\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e2.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.045 \u0026ndash; 4.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.0379*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.353 \u0026ndash; 3.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.9284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.311 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.451 \u0026ndash; 7.554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.0044*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.723 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.825 \u0026ndash; 8.983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.1001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eHigh RF titer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e4.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2.082 \u0026ndash; 9.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.840 \u0026ndash; 5.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eLower anti-CCP Ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.153 \u0026ndash; 0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.0252*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.200 \u0026ndash; 1.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.3155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e5.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e2.413 \u0026ndash; 11.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e4.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.426 \u0026ndash; 11.223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0084*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e3.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e1.808 \u0026ndash; 8.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.755 \u0026ndash; 5.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.1636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eTNF\u0026alpha;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e8.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e3.556 \u0026ndash; 19.150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 10px;\"\u003e\n \u003cp\u003e6.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2.607 \u0026ndash; 18.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20px;\"\u003e\n \u003cp\u003eBT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e0.377 \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.149 \u0026ndash; 0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.0392*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 2px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e0.113 \u0026ndash; 0.990\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.0479*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eBT（Biologic therapies） including TNF-\u0026alpha; inhibitors, IL-6 inhibitors, or the CTLA-4 fusion protein abatacept . *p\u0026lt;0.05.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rheumatoid arthritis, interstitial lung disease, Risk Factor, Cytokine","lastPublishedDoi":"10.21203/rs.3.rs-7500736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7500736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInterstitial lung disease (ILD) is a severe extra-articular manifestation of rheumatoid arthritis (RA). This study identified risk factors and developed a predictive model for RA-ILD in 208 RA patients from the Second Affiliated Hospital of Soochow University (2010\u0026ndash;2023). ILD was confirmed via high-resolution computed tomography (HRCT). Logistic regression and ROC curve analyses determined optimal biomarker thresholds: rheumatoid factor (RF)\u0026thinsp;\u0026gt;\u0026thinsp;352.5 IU/mL, anti-CCP antibodies\u0026thinsp;\u0026gt;\u0026thinsp;43.25 IU/mL, complement C3\u0026thinsp;\u0026lt;\u0026thinsp;0.765 g/L, C4\u0026thinsp;\u0026lt;\u0026thinsp;0.1935 g/L, and TNF-α\u0026thinsp;\u0026gt;\u0026thinsp;1.7295 pg/mL. Univariate analysis linked male gender, smoking, elevated RF/anti-CCP, low C3/C4, high TNF-α, and reduced biologic therapy to ILD (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Multivariate analysis confirmed C3, TNF-α, and biologic therapy as independent predictors (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The nomogram demonstrated strong discrimination (C-index 0.829, 95% CI 0.756\u0026ndash;0.902). RA-ILD exhibits distinct features (male predominance, smoking, dysregulated immunity), while biologic therapy may be protective. This model aids early risk stratification and clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Risk Factors Associated with Interstitial Lung Disease in Rheumatoid Arthritis: A Monocentric Clinical and Serological Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 06:48:47","doi":"10.21203/rs.3.rs-7500736/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f732ce0f-e4ba-4c48-8e62-f21b73e641c1","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-31T08:54:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-30 06:48:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7500736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7500736","identity":"rs-7500736","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}