Analysis of risk factors and development of a nomogram prediction model for tubular acidosis in primary Sjogren syndrome patients

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Method Data from 99 pSS patients who underwent inpatient treatment at our hospital from January 2012 to January 2024 were retrospectively collected and analyzed. Bootstrap resampling technique, single-factor, and multi-factor logistic regression analyses were used to explore the risk factors for pSS-RTA. A nomogram was developed based on the results of the multivariate logistic model. The model was evaluated through receiver operating characteristic curve, C-index, calibration curve, and decision curve analysis . Results A multivariate logistic regression analysis revealed that concurrent thyroid disease, long symptom duration, subjective dry mouth, and positive RF were independent risk factors for pSS-RTA patients. Based on them, a personalized nomogram predictive model was established. With a p-value of 0.657 from the Hosmer-Lemeshow test, the model demonstrated a good fit. The AUC values in the training and validation groups were 0.912 and 0.896, indicating a strong discriminative power of the nomogram. The calibration curves for the training and validation groups closely followed the diagonal line with a slope of 1, confirming the model’s reliable predictive ability. Furthermore, the decision curve analysis showed that the nomogram model had a net benefit in predicting pSS-RTA, emphasizing its clinical value. Discussion We developed a nomogram to predict RTA occurrence in pSS patients, and it is believed to provide a foundation for early identification and intervention for high-risk pSS patients. primary Sjögren’s syndrome renal tubular acidosis nomogram risk factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sjogren’s syndrome (SS) is a progressive systemic autoimmune disease that develops slowly, being one of the most common autoimmune diseases with an estimated prevalence of around 0.1%-4.8%. Due to its slow progression, the rate of diagnosis is low. This disease can occur either independently (primary Sjogren’s syndrome, pSS) or in conjunction with other autoimmune diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), dermatomyositis (DM), or systemic sclerosis (SSc)(secondary Sjogren’s syndrome, sSS). The primary affected demographic includes middle-aged women, with a gender ratio of 1:9, making it a significant public health issue. The hallmark feature of the syndrome is the infiltration of lymphocytes in the exocrine glands, including the salivary and tear glands, leading to dysfunction of these glands and causing symptoms such as dry mouth and dry eyes[ 1 – 3 ]. Apart from affecting the exocrine glands, Sjogren’s syndrome can also impact various organs and systems in the body, including the kidneys, lungs, thyroid, heart, blood system, nervous system, and digestive system, resulting in corresponding symptoms[ 4 ]. A large retrospective study in China[ 5 ] found a higher prevalence of kidney involvement in Chinese pSS patients compared to other countries, at 33.5%. Renal manifestations related to pSS range from mild electrolyte abnormalities to complete distal renal tubular acidosis (cRTA), interstitial nephritis (IN), and glomerulonephritis (GN) [ 6 ], with renal tubular acidosis (RTA) being the most prevalent. In recent years, an increasing number of clinicians have observed a higher incidence of RTA in pSS patients, sometimes occurring even before the onset of pSS. They highlighted in case reports [ 7 – 9 ]that many pSS patients presenting symptoms related to hypokalemia due to RTA seek treatment in other departments, posing challenges for the early diagnosis and management of Sjogren’s syndrome. Without prompt treatment, this might even endanger the patients’lives. Hence, early identification of pSS-RTA holds substantial clinical value in improving patient prognosis, such as preventing fractures, life-threatening muscle paralysis, and chronic kidney disease. The etiology and pathogenesis of pSS-RTA remain unclear. Pertovaara[ 10 ] and others detected autoantibodies against carbonic anhydrase in the serum of Sjogren’s syndrome patients, which seemed to be connected with RTA. Animal studies[ 11 ] have also shown that inducing anti-carbonic anhydrase in mice can lead to the development of pSS-RTA. However, it is not yet clear whether these autoantibodies result from or cause renal damage. Studies suggest a relationship between α-intercalated cell vesicle H+-ATPase and anion exchanger I deficiency and pSS-RTA[ 6 ]. RTA and exocrine gland involvement share common pathogenic mechanisms and histological characteristics[ 12 ]. Some targets in pSS-RTA, such as carbonic anhydrase II and H+-ATPase, are expressed in salivary glands and kidney intercalated cells[ 6 ]. Current clinical analyses of pSS-RTA risk factors are limited and not without controversy. Jain et al. [ 13 ] reported a lower incidence of dry eyes in pSS-RTA patients compared to those without RTA, with a similar rate of dry mouth occurrence. Conversely, a meta-analysis showed no significant correlation between renal involvement in pSS and anti-SSA antibodies, rheumatoid factor, dry eye syndrome, or labial salivary gland biopsy[ 14 ]. Synthesizing previous studies[ 13 , 15 – 17 ], it is revealed that several factors are associated with pSS-RTA, including a younger age of onset, longer disease duration, subjective dry mouth, arthritis, EULAR disease activity index, decreased glomerular filtration rate, thyroid disease, anemia, elevated alkaline phosphatase levels, decreased albumin levels, increased erythrocyte sedimentation rate, anti-SSA and anti-SSB antibodies, and high gamma globulin levels. However, these studies are mostly based on small samples and are not specific to pSS-RTA, underscoring the need for further clarification on the relationship between demographic characteristics, laboratory indicators, clinical features of pSS patients, and RTA risk. In clinical practice, physicians often require tools to aid in the identification of pSS-RTA. In recent years, the use of online tools for prognosis and risk prediction of diseases has become popular among clinicians and patients, with the nomogram widely used as a predictive method for various types of cancers[ 18 – 21 ]. Predictive models serve as valuable tools to guide clinical practitioners in considering the uniqueness of individual pSS patients and making appropriate treatment decisions. Therefore, we conducted a systematic review of the medical records of pSS patients over the years to explore the clinical and laboratory features of pSS patients with and without RTA, aiming to identify risk factors for pSS-RTA and establish a risk prediction model based on demographic, clinical features, and laboratory indicators. Our study results will offer guidance for clinical practice. Materials and Methods Patients We conducted a retrospective analysis of data from 99 patients with pSS who received inpatient treatment at the Affiliated Hospital of Xuzhou Medical University from January 2012 to January 2024. Patients were categorized into two groups: pSS-RTA group (37 cases) and pSS group (62 cases) based on the presence of renal tubular acidosis (RTA). RTA was defined by the following criteria ≥ 1: (1) metabolic acidosis with high chloride levels, normal anion gap, urine pH > 5.5, and positive urine anion gap; (2) abnormal results on the ammonium chloride loading test[ 22 ]. Inclusion criteria were: (1) age ≥ 18 years; (2) patients meeting the 2002 American-European Consensus Group (AECG) [ 23 ] or the 2016 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for primary Sjögren’s syndrome [ 24 ]; (3) initial visit and treatment without medication interference. Exclusion criteria were: (1) age < 18 years; (2) patients with irreversible kidney damage, end-stage renal disease, kidney malformations, or other known causes of RTA such as genetic disorders, drug-related RTA, or hypercalcemia; (3) patients with rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, or other autoimmune diseases; (4) patients with cancer; (5) pregnant or lactating women; (6) patients with significant missing clinical data. Data collection Collecting the demographic characteristics of patients involves gender, age, body mass index (BMI), and disease duration; clinical symptoms include subjective dry eyes, subjective dry mouth, tooth loss, fever, and joint pain; concurrent conditions encompass thyroid disease, diabetes, hypertension, and infections; laboratory parameters comprise white blood cell count, red blood cell count, hemoglobin, platelet count, erythrocyte sedimentation rate, C-reactive protein, alkaline phosphatase, IgG, IgA, IgM, complement C3, complement C4, ANA, RF, SSA antibodies, RO52 antibodies, and SSB antibodies. The duration of symptoms is measured from symptom onset to initial diagnosis; thyroid disease encompasses hyperthyroidism, hypothyroidism, and thyroiditis. Data processing And Statistical Analysis Exclude variables with a proportion of missing values and outliers exceeding 5%. Split the data into a training group (n = 80) and a validation group (n = 19) at an 8:2 ratio through random assignment. Normally distributed data are presented as mean ± standard deviation (mean ± SD) for comparison using independent sample t-tests. Non-normally distributed data are described using the median and quartiles 25% and 75% [M (Q1, Q3)], with comparisons done through the Mann-Whitney U test. Categorical data are displayed as percentages (%), compared using the χ2 test or Fisher’s exact test. Variables with a p-value < 0.05 in the univariate analysis are considered statistically significant. Multicollinearity is assessed using variance inflation factor and tolerance, showing no issues detected. Given the small sample size, bootstrapping is implemented in univariate analysis by resampling the data 1000 times to mitigate errors due to the limited sample size. Identified significant factors from the univariate analysis are further examined in a multivariable logistic regression to construct a nomogram based on the multivariable logistic model results. The model’s discriminatory ability in the training set is evaluated using receiver operating characteristic (ROC) curves and the C-index. Calibration curves are introduced for validation of predictive performance, and decision curve analysis (DCA) is employed to assess the new model’s clinical utility. Statistical analysis is performed utilizing SPSS (version 22.0) and R software (version 4.3). A significance level of P < 0.05 is deemed statistically significant. Results Baseline Demographic and Clinical Features In this study, 80 patients were included in the training group (7 males, 73 females; with an average age of 44.95 ± 14.54 years and average BMI of 21.83 ± 2.65; median illness duration of 3 months), while the validation group consisted of 19 patients (1 male, 19 females; average age 48.65 ± 14.62 years, average BMI 22.06 ± 3.16; median illness duration of 4 months). Among the 99 patients, 11 had concurrent infections, 6 had a history of hypertension, 3 had a history of diabetes, and 31 had thyroid diseases. The training group’s baseline data encompassed subjective dry eyes, subjective dry mouth, tooth loss, fever, joint pain, blood cell counts, hemoglobin levels, platelet counts, inflammatory markers, and various antibodies as detailed in Table 1 . Comparability between the training and validation groups was confirmed with no statistically significant differences observed in the baseline clinical data, as presented in Table S1 . Table 1 Baseline clinical features between pSS group and pSS-RTA group. Variables pSS group(n = 50) pSS-RTA group(n = 30) p Age(year) 46.06 ± 14.89 43.1 ± 13.98 0.374 Sex 0.706 Female 45 (90) 28 (93) Man 5 (10) 2 (7) BMI(kg/m2) 22.46 ± 2.61 20.79 ± 2.41 0.005 Infection 0.164 No 46 (92) 24 (80) Yes 4 (8) 6 (20) Hypertension 0.645 No 46 (92) 29 (97) Yes 4 (8) 1 (3) Diabetes 0.553 No 49 (98) 28 (93) Yes 1 (2) 2 (7) Thyroid dysfunction 0.028 No 41 (82) 17 (57) Yes 9 (18) 13 (43) Symptom duration(month) 2 (0.45, 12) 6 (0.55, 84) 0.244 Dry mouth < 0.001 No 34 (68) 7 (23) Yes 16 (32) 23 (77) Dry eyes 0.246 No 36 (72) 17 (57) Yes 14 (28) 13 (43) Rampant tooth 0.073 No 47 (94) 24 (80) Yes 3 (6) 6 (20) Fever 0.722 No 45 (90) 26 (87) Yes 5 (10) 4 (13) Arthrodynia 1 No 40 (80) 24 (80) Yes 10 (20) 6 (20) WBC(1*109) 5.45 (3.6, 7.3) 5.7 (4.12, 7.55) 0.444 RBC(1*1012) 3.91 ± 0.69 3.98 ± 0.72 0.688 Hb(g/L) 118.5 (108, 131.5) 116 (106, 127.75) 0.474 PLT(1*109) 182.56 ± 101.12 233.5 ± 103.83 0.036 ESR(mm/L) 48.82 (23, 54.95) 48.82 (42.2, 52.5) 0.34 CRP(mg/L) 5.28 (2.06, 16.62) 6.54 (2.21, 15.66) 0.834 Alkaline phosphatase(U/L) 74 (61.75, 89.5) 100.5 (72.75, 140.75) 0.003 IgG(g/L) 0.77 (0.7, 0.92) 1.06 (1, 1.42) < 0.001 IgA(g/L) 18.85 (14.25, 20.57) 20.17 (17.58, 26.4) 0.096 IgM(g/L) 3.02 (2.29, 3.54) 3.02 (2.65, 4.26) 0.108 C3(g/L) 1.64 (1, 1.83) 1.29 (1.05, 1.64) 0.105 C4(g/L) 0.92 ± 0.19 0.92 ± 0.22 0.981 RF 0.2 (0.17, 0.24) 0.2 (0.17, 0.22) 0.495 - < 0.001 + 21 (42) 1 (3) ANA 29 (58) 29 (97) - 0.375 + 0 (0) 1 (3) Anti-SSA 50 (100) 29 (97) - 1 + 1 (2) 0 (0) Anti-RO52 49 (98) 30 (100) - 0.041 + 7 (14) 0 (0) Anti-SSB 43 (86) 30 (100) - 0.003 + 32 (64) 8 (27) The variables mentioned were all collected at baseline. Nomogram development Significant baseline data were subjected to a univariate logistic analysis, highlighting BMI, thyroid diseases, duration of symptoms, subjective dry mouth, platelet count, alkaline phosphatase, RF, and SSB antibodies as statistically meaningful risk factors (Table 2 ). To address selection bias due to the high prevalence of thrombocytopenia in hospitalized pSS patients, platelet count was excluded. Subsequently, a multivariate logistic analysis was conducted to further evaluate the significant risk factors identified in the univariate analysis. The findings confirmed the statistical significance of thyroid diseases, symptom duration, subjective dry mouth, and RF in the multivariable logistic regression. Leveraging the regression coefficients from the multivariate logistic analysis, an individualized nomogram prediction model for pSS-RTA was formulated (Fig. 1 ). The nomogram assigns scores to each risk factor, and the cumulative score corresponds to the predicted risk of pSS patients developing RTA. Table 2 Univariate and multivariate logistic regression of development set. Factors Univariate analysis Multivariate analysis OR 95% CI P value OR 95% CI P value BMI(kg/m2) 0.752 0.607–0.932 0.009 Thyroid dysfunction 3.484 1.255–9.668 0.017 9.982 1.8-55.369 0.008 Symptom duration(month) 1.017 1.003–1.03 0.013 1.017 1.001–1.034 0.04 Dry mouth 6.982 2.483–19.633 0.000 17.843 3.464–91.903 0.001 PLT(1*109) 1.005 1-1.01 0.039 Alkaline phosphatase(U/L) 1.008 1-1.015 0.051 RF 21 2.647-166.599 0.004 25.208 2.412–263.47 0.007 Anti-SSB 4.889 1.809–13.211 0.002 Nomogram validation The effectiveness and calibration of the predictive model were assessed through the area under the curve (AUC) of ROC and calibration plots. Receiver operating characteristic curves (ROC) were constructed, and the AUC values were computed for both the training and validation datasets. The Hosmer-Lemeshow test resulted in a p-value of 0.657, indicating a good model fit. The AUC values for predicting combined RTA risk in the training and validation groups were 0.912 and 0.896, respectively (Fig. 2), highlighting the nomogram’s strong discriminative capacity. The calibration curves for the training and validation groups (solid lines in Fig. 3) closely followed the ideal prediction line with a slope of 1 (dashed diagonal line), indicating the model’s reliable predictive performance. Moreover, decision curve analysis demonstrated a positive net benefit in predicting RTA risk in pSS patients using the Nomogram model, underlining its clinical relevance in RTA risk prediction (Fig. 4). Discussion In primary Sjögren’s syndrome (pSS) patients, the kidneys are commonly affected. Chronic and acute tubulointerstitial nephritis are the most common manifestations, often leading to renal tubular acidosis (RTA) clinically, similar to exocrine glands, due to lymphocyte infiltration around the kidney tubules[ 25 ]. However, pSS-RTA typically starts insidiously, presenting few noticeable symptoms apart from electrolyte imbalances, elevated creatinine, and proteinuria. Nevertheless, the complications of fractures, life-threatening muscle paralysis, and chronic kidney disease are significant [ 6 ], underscoring the importance of early identification of pSS-RTA patients for timely intervention, improved prognosis, and enhanced quality of life. Prior studies have examined potential risk factors in pSS-RTA patients, encompassing demographics, clinical features, and laboratory parameters [ 13 – 17 ]. Nevertheless, there has been a lack of studies specifically tackling pSS-RTA and developing a model that integrates multiple laboratory findings into clinical decision-making. In this study, we constructed a prediction model for pSS-RTA using both univariate and multivariate logistic regression analyses, visually represented as a graph to aid in assessing the risk of RTA in pSS patients. Our research pinpointed clinical characteristics linked to pSS-RTA, including BMI, thyroid disease, duration of symptoms, subjective dry mouth, platelet count, alkaline phosphatase, RF, and SSB antibodies. The multifactorial logistic regression unveiled that concomitant thyroid disease, prolonged symptom duration, subjective dry mouth, and positive RF are independent risk factors for pSS-RTA. Subsequently, we devised and validated a straightforward model to predict RTA in pSS patients. Our study outcomes may help clinicians recognize these risk factors to facilitate early detection, diagnosis, and treatment of pSS-RTA patients, thus enhancing their prognosis and quality of life. Our group of pSS-RTA patients has an average age of 41 years, which is approximately 10 years younger than European pSS patients with kidney involvement (average age 52 years), and similar to previously reported Indian (average age 40.19 years) and Chinese cohorts (average age 40.1 years) [ 26 – 28 ]. This confirms that kidney involvement in Asian populations with pSS tends to occur at a younger age compared to Western countries. The link between thyroid disease and systemic autoimmune diseases is well-known. Among 5 patients with both thyroid disease and renal tubular acidosis, 3 also had Sjögren’s syndrome, indicating a possible connection among these conditions, although the exact mechanism remains unclear [ 29 ]. A multicenter study involving 4479 pSS patients found that thyroid disease (OR 1.49, 95% CI 1.04–2.14) is an independent risk factor for pSS-RTA [ 28 ]. Our study also supports the association between thyroid disease and pSS-RTA, emphasizing the importance of screening young women with thyroid disease for autoimmune conditions, especially pSS. Similarly, diagnosed pSS patients should be screened for thyroid disease to enable early detection and intervention. Our findings suggest that pSS-RTA patients experience symptoms for a longer duration than pSS patients, highlighting a diagnostic delay possibly due to some RTA patients being undiagnosed due to mild symptoms. Additionally, symptoms related to low potassium levels may lead patients to seek care in specialties such as nephrology, gastroenterology, or endocrinology, contributing to delays in pSS diagnosis. Therefore, thorough autoimmune disease screening, particularly for pSS, is recommended for young women with RTA. There is debate regarding whether there are differences in dry mouth symptoms between pSS patients with and without RTA. Jain et al. reported a lower occurrence of dry eyes in pSS-RTA patients compared to those without RTA, while the difference in dry mouth occurrence was not statistically significant [ 13 ]. In our study, dry mouth was more prevalent in pSS-RTA patients than in those without RTA, while the occurrence of dry eyes was similar. One possible explanation is that RTA and exocrine gland involvement share common pathogenic mechanisms and histological characteristics. Certain targets of pSS-RTA, such as carbonic anhydrase II and H ± ATPase, are expressed in both salivary glands and renal intercalated cells [ 6 ]. Another explanation could be that patients with renal tubular dysfunction may develop nephrogenic diabetes insipidus, leading to polydipsia and subjective dry mouth symptoms. We also discovered a positive correlation between RF positivity and the risk of pSS patients developing RTA. This is the first report indicating a link between RF positivity and pSS-RTA. RF, one of the autoantibodies associated with pSS, is an immunoglobulin with varying isotypes and affinities first discovered over 80 years ago, although its mechanisms and pathophysiology are not fully understood [ 30 ]. RF is commonly found in patients with RA, connective tissue diseases, and various infections, and occasionally in healthy individuals. Studies have shown RF positivity in 75–95% of pSS patients [ 31 ]. Recent research has revealed RF positivity as an independent predictor of lymphoma in pSS patients, underscoring its critical role in lymphoma development [ 32 , 33 ]. There is evidence suggesting that RF positivity is associated with more severe and prolonged disease in RA patients[ 34 ]. These findings collectively demonstrate the significant role of RF in the occurrence, development, and prediction of autoimmune diseases. While more research has focused on RF in the RA field, there is a need for further exploration of RF in the context of pSS to identify new therapeutic targets and predict the evolution of pSS-related complications more accurately. This study has limitations, including a relatively small number of eligible patients due to strict inclusion criteria, the retrospective nature of the study introducing potential biases, and single-center data limiting generalizability. Moreover, the lack of time-series data in the predictive model hinders accurate risk assessment for pSS patients developing RTA. Future plans involve expanding the sample size in collaboration with multiple hospitals to establish a Nomogram with additional clinical predictors and time-series data. Conclusions In conclusion, our Nomogram for predicting the likelihood of pSS patients developing RTA exhibits good discrimination, calibration, and clinical benefits. Utilizing this Nomogram can guide early identification of high-risk patients and prompt intervention for better outcomes. Abbreviations ACR American College of Rheumatology ANA antinuclear antibody AUC area under the curve BMI body mass index DCA decision curve analysis DM dermatomyositis EULAR European League Against Rheumatism GN glomerulonephritis IgA Immunoglobulin A IgG Immunoglobulin G IgM Immunoglobulin M IN interstitial nephritis pSS primary Sjogren’s syndrome RA rheumatoid arthritis RF rheumatoid factor ROC receiver operating characteristic RTA renal tubular acidosis SLE systemic lupus erythematosus SS Sjogren’s syndrome sSS secondary Sjogren’s syndrome SSc systemic sclerosis Declarations Acknowledgements Not applicable Authors’ contributions Yanzhen Zeng participated in the study design and analysis and wrote the manuscript. Runzhi Liu and Shuyi Li participated in study design and manuscript revision. Jingwen Wei and Fei Luo wrote the main manuscript tables and figures.Yongkang Chen and Dongmei Zhou participated in manuscript revision. Funding Not applicable Availability of data and materials Data and material are available from the corresponding author upon reasonable request. Ethics approval and consent to participate This study was approved by the Ethics Committee of Xuzhou Medical University Affiliated Hospital and the committee’s reference number is XYFY2024KL-202. Consent for publication Not applicable. Competing interests The authors declare no competing interests References Mavragani, C.P. and H.M. Moutsopoulos, The geoepidemiology of Sjögren's syndrome. Autoimmun Rev, 2010. 9 (5): p. A305-10. Chatzis, L., et al., New frontiers in precision medicine for Sjogren's syndrome. Expert Rev Clin Immunol, 2021. 17 (2): p. 127-141. Qin, B., et al., Epidemiology of primary Sjögren's syndrome: a systematic review and meta-analysis. Ann Rheum Dis, 2015. 74 (11): p. 1983-9. 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Ingegnoli, F., R. Castelli, and R. Gualtierotti, Rheumatoid factors: clinical applications. Dis Markers, 2013. 35 (6): p. 727-34. Additional Declarations No competing interests reported. Supplementary Files TableS1.pdf Cite Share Download PDF Status: Published Journal Publication published 22 Aug, 2024 Read the published version in Arthritis Research & Therapy → Version 1 posted Editorial decision: Revision requested 13 Jul, 2024 Reviews received at journal 01 Jul, 2024 Reviews received at journal 28 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 08 Jun, 2024 Editor assigned by journal 23 May, 2024 Submission checks completed at journal 23 May, 2024 First submitted to journal 21 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4453751","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":310130744,"identity":"ce135d75-99a8-4548-bbfa-844159d104b6","order_by":0,"name":"Yanzhen Zeng","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanzhen","middleName":"","lastName":"Zeng","suffix":""},{"id":310130745,"identity":"ff99e8e2-ab28-4fa3-aff5-0171166af3f3","order_by":1,"name":"Runzhi Liu","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Runzhi","middleName":"","lastName":"Liu","suffix":""},{"id":310130746,"identity":"bc339feb-a763-4ff5-a741-1b3bd00e7643","order_by":2,"name":"Shuyi Li","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuyi","middleName":"","lastName":"Li","suffix":""},{"id":310130747,"identity":"7e1ef03e-22ea-4e7b-b284-bbec1888ad3c","order_by":3,"name":"Jingwen Wei","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Wei","suffix":""},{"id":310130748,"identity":"4ae7c004-e9bc-494a-91e6-e8abf0af265f","order_by":4,"name":"Fei Luo","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Luo","suffix":""},{"id":310130749,"identity":"6183ea08-85e3-4d39-b0b1-b55ba66a4242","order_by":5,"name":"Yongkang Chen","email":"","orcid":"","institution":"Xuzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yongkang","middleName":"","lastName":"Chen","suffix":""},{"id":310130750,"identity":"bb1274f7-e5eb-4eca-bf20-8b65859b68a6","order_by":6,"name":"Dongmei Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYFCCBMYHDAwHwEwJYrUwG5CshU2CNC3y7dlp1Tx/7kQbHGA+eJuHwS6PoBbGnrfbbs7geZa74QBbsjUPQ3IxQS3MErnbbnyQOAzUwmMmzcNwILGBkBY2oJaCBAOQFv5vxGnhAWph+JAAtoWNOC0SPG83S844cDh35mE2Y8s5BsmEtci35278zPPncG7f8eaHN95U2BHWggDMIMKAePWjYBSMglEwCvAAAPfoPklt0j/uAAAAAElFTkSuQmCC","orcid":"","institution":"The Affiliated Hospital Of Xuzhou Medical University","correspondingAuthor":true,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2024-05-21 09:32:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4453751/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4453751/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13075-024-03383-w","type":"published","date":"2024-08-22T15:57:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57952510,"identity":"bd5904cc-7d1c-463c-a8f1-acb141207abe","added_by":"auto","created_at":"2024-06-07 22:57:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49752,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting the probability of pSS-RTA patients.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4453751/v1/e08589d6ffd6f715b3154744.jpg"},{"id":57952511,"identity":"3d765ba7-ce4c-4114-a681-96a18f876a5e","added_by":"auto","created_at":"2024-06-07 22:57:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105652,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves (ROC) of the development set and validation set. (A) Development set. (B) Validation set (AUC = 0.912 vs. 0.896).\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4453751/v1/2434995219af8b681eccfaa6.jpg"},{"id":57953563,"identity":"7e4c58f4-e681-4a74-bf02-4603c3ea0b03","added_by":"auto","created_at":"2024-06-07 23:05:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":73269,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots of the development set and validation set. (A) Development set. (B) Validation set.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4453751/v1/3ea4d133fadc2a34409c2634.jpg"},{"id":57952514,"identity":"07c3d048-9f71-45a4-8f78-dc99a47563c3","added_by":"auto","created_at":"2024-06-07 22:57:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":96417,"visible":true,"origin":"","legend":"\u003cp\u003eDecision curve analysis for the development set and validation set. (A) Development set. (B) Validation set.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4453751/v1/b7589066abb977abf576fca9.jpg"},{"id":63300061,"identity":"ee17cd6b-5895-4cff-88d7-3005e53c13ca","added_by":"auto","created_at":"2024-08-26 16:10:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":962715,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4453751/v1/f328eee9-e557-4d18-8ef7-683790f323fc.pdf"},{"id":57952513,"identity":"d0ae0dc1-e08c-4d80-9aea-3b166248c5ad","added_by":"auto","created_at":"2024-06-07 22:57:22","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":65794,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4453751/v1/68b13997189d7caa297f3bca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of risk factors and development of a nomogram prediction model for tubular acidosis in primary Sjogren syndrome patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSjogren\u0026rsquo;s syndrome (SS) is a progressive systemic autoimmune disease that develops slowly, being one of the most common autoimmune diseases with an estimated prevalence of around 0.1%-4.8%. Due to its slow progression, the rate of diagnosis is low. This disease can occur either independently (primary Sjogren\u0026rsquo;s syndrome, pSS) or in conjunction with other autoimmune diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), dermatomyositis (DM), or systemic sclerosis (SSc)(secondary Sjogren\u0026rsquo;s syndrome, sSS). The primary affected demographic includes middle-aged women, with a gender ratio of 1:9, making it a significant public health issue. The hallmark feature of the syndrome is the infiltration of lymphocytes in the exocrine glands, including the salivary and tear glands, leading to dysfunction of these glands and causing symptoms such as dry mouth and dry eyes[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Apart from affecting the exocrine glands, Sjogren\u0026rsquo;s syndrome can also impact various organs and systems in the body, including the kidneys, lungs, thyroid, heart, blood system, nervous system, and digestive system, resulting in corresponding symptoms[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA large retrospective study in China[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] found a higher prevalence of kidney involvement in Chinese pSS patients compared to other countries, at 33.5%. Renal manifestations related to pSS range from mild electrolyte abnormalities to complete distal renal tubular acidosis (cRTA), interstitial nephritis (IN), and glomerulonephritis (GN) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], with renal tubular acidosis (RTA) being the most prevalent. In recent years, an increasing number of clinicians have observed a higher incidence of RTA in pSS patients, sometimes occurring even before the onset of pSS. They highlighted in case reports [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]that many pSS patients presenting symptoms related to hypokalemia due to RTA seek treatment in other departments, posing challenges for the early diagnosis and management of Sjogren\u0026rsquo;s syndrome. Without prompt treatment, this might even endanger the patients\u0026rsquo;lives. Hence, early identification of pSS-RTA holds substantial clinical value in improving patient prognosis, such as preventing fractures, life-threatening muscle paralysis, and chronic kidney disease.\u003c/p\u003e \u003cp\u003eThe etiology and pathogenesis of pSS-RTA remain unclear. Pertovaara[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and others detected autoantibodies against carbonic anhydrase in the serum of Sjogren\u0026rsquo;s syndrome patients, which seemed to be connected with RTA. Animal studies[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] have also shown that inducing anti-carbonic anhydrase in mice can lead to the development of pSS-RTA. However, it is not yet clear whether these autoantibodies result from or cause renal damage. Studies suggest a relationship between α-intercalated cell vesicle H+-ATPase and anion exchanger I deficiency and pSS-RTA[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. RTA and exocrine gland involvement share common pathogenic mechanisms and histological characteristics[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Some targets in pSS-RTA, such as carbonic anhydrase II and H+-ATPase, are expressed in salivary glands and kidney intercalated cells[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Current clinical analyses of pSS-RTA risk factors are limited and not without controversy. Jain et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reported a lower incidence of dry eyes in pSS-RTA patients compared to those without RTA, with a similar rate of dry mouth occurrence. Conversely, a meta-analysis showed no significant correlation between renal involvement in pSS and anti-SSA antibodies, rheumatoid factor, dry eye syndrome, or labial salivary gland biopsy[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Synthesizing previous studies[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], it is revealed that several factors are associated with pSS-RTA, including a younger age of onset, longer disease duration, subjective dry mouth, arthritis, EULAR disease activity index, decreased glomerular filtration rate, thyroid disease, anemia, elevated alkaline phosphatase levels, decreased albumin levels, increased erythrocyte sedimentation rate, anti-SSA and anti-SSB antibodies, and high gamma globulin levels. However, these studies are mostly based on small samples and are not specific to pSS-RTA, underscoring the need for further clarification on the relationship between demographic characteristics, laboratory indicators, clinical features of pSS patients, and RTA risk.\u003c/p\u003e \u003cp\u003eIn clinical practice, physicians often require tools to aid in the identification of pSS-RTA. In recent years, the use of online tools for prognosis and risk prediction of diseases has become popular among clinicians and patients, with the nomogram widely used as a predictive method for various types of cancers[\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Predictive models serve as valuable tools to guide clinical practitioners in considering the uniqueness of individual pSS patients and making appropriate treatment decisions. Therefore, we conducted a systematic review of the medical records of pSS patients over the years to explore the clinical and laboratory features of pSS patients with and without RTA, aiming to identify risk factors for pSS-RTA and establish a risk prediction model based on demographic, clinical features, and laboratory indicators. Our study results will offer guidance for clinical practice.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003ePatients\u003c/p\u003e \u003cp\u003eWe conducted a retrospective analysis of data from 99 patients with pSS who received inpatient treatment at the Affiliated Hospital of Xuzhou Medical University from January 2012 to January 2024. Patients were categorized into two groups: pSS-RTA group (37 cases) and pSS group (62 cases) based on the presence of renal tubular acidosis (RTA). RTA was defined by the following criteria\u0026thinsp;\u0026ge;\u0026thinsp;1: (1) metabolic acidosis with high chloride levels, normal anion gap, urine pH\u0026thinsp;\u0026gt;\u0026thinsp;5.5, and positive urine anion gap; (2) abnormal results on the ammonium chloride loading test[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Inclusion criteria were: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) patients meeting the 2002 American-European Consensus Group (AECG) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] or the 2016 American College of Rheumatology (ACR)/European League Against Rheumatism (EULAR) classification criteria for primary Sj\u0026ouml;gren\u0026rsquo;s syndrome [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; (3) initial visit and treatment without medication interference. Exclusion criteria were: (1) age\u0026thinsp;\u0026lt;\u0026thinsp;18 years; (2) patients with irreversible kidney damage, end-stage renal disease, kidney malformations, or other known causes of RTA such as genetic disorders, drug-related RTA, or hypercalcemia; (3) patients with rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, or other autoimmune diseases; (4) patients with cancer; (5) pregnant or lactating women; (6) patients with significant missing clinical data.\u003c/p\u003e \u003cp\u003eData collection\u003c/p\u003e \u003cp\u003eCollecting the demographic characteristics of patients involves gender, age, body mass index (BMI), and disease duration; clinical symptoms include subjective dry eyes, subjective dry mouth, tooth loss, fever, and joint pain; concurrent conditions encompass thyroid disease, diabetes, hypertension, and infections; laboratory parameters comprise white blood cell count, red blood cell count, hemoglobin, platelet count, erythrocyte sedimentation rate, C-reactive protein, alkaline phosphatase, IgG, IgA, IgM, complement C3, complement C4, ANA, RF, SSA antibodies, RO52 antibodies, and SSB antibodies. The duration of symptoms is measured from symptom onset to initial diagnosis; thyroid disease encompasses hyperthyroidism, hypothyroidism, and thyroiditis.\u003c/p\u003e \u003cp\u003eData processing And Statistical Analysis\u003c/p\u003e \u003cp\u003eExclude variables with a proportion of missing values and outliers exceeding 5%. Split the data into a training group (n\u0026thinsp;=\u0026thinsp;80) and a validation group (n\u0026thinsp;=\u0026thinsp;19) at an 8:2 ratio through random assignment. Normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) for comparison using independent sample t-tests. Non-normally distributed data are described using the median and quartiles 25% and 75% [M (Q1, Q3)], with comparisons done through the Mann-Whitney U test. Categorical data are displayed as percentages (%), compared using the χ2 test or Fisher\u0026rsquo;s exact test. Variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis are considered statistically significant. Multicollinearity is assessed using variance inflation factor and tolerance, showing no issues detected. Given the small sample size, bootstrapping is implemented in univariate analysis by resampling the data 1000 times to mitigate errors due to the limited sample size. Identified significant factors from the univariate analysis are further examined in a multivariable logistic regression to construct a nomogram based on the multivariable logistic model results. The model\u0026rsquo;s discriminatory ability in the training set is evaluated using receiver operating characteristic (ROC) curves and the C-index. Calibration curves are introduced for validation of predictive performance, and decision curve analysis (DCA) is employed to assess the new model\u0026rsquo;s clinical utility. Statistical analysis is performed utilizing SPSS (version 22.0) and R software (version 4.3). A significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is deemed statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBaseline Demographic and Clinical Features\u003c/p\u003e\n\u003cp\u003eIn this study, 80 patients were included in the training group (7 males, 73 females; with an average age of 44.95\u0026thinsp;\u0026plusmn;\u0026thinsp;14.54 years and average BMI of 21.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.65; median illness duration of 3 months), while the validation group consisted of 19 patients (1 male, 19 females; average age 48.65\u0026thinsp;\u0026plusmn;\u0026thinsp;14.62 years, average BMI 22.06\u0026thinsp;\u0026plusmn;\u0026thinsp;3.16; median illness duration of 4 months). Among the 99 patients, 11 had concurrent infections, 6 had a history of hypertension, 3 had a history of diabetes, and 31 had thyroid diseases. The training group\u0026rsquo;s baseline data encompassed subjective dry eyes, subjective dry mouth, tooth loss, fever, joint pain, blood cell counts, hemoglobin levels, platelet counts, inflammatory markers, and various antibodies as detailed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Comparability between the training and validation groups was confirmed with no statistically significant differences observed in the baseline clinical data, as presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline clinical features between pSS group and pSS-RTA group.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epSS group(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003epSS-RTA group(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46.06\u0026thinsp;\u0026plusmn;\u0026thinsp;14.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.374\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.79\u0026thinsp;\u0026plusmn;\u0026thinsp;2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInfection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThyroid dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSymptom duration(month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (0.45, 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (0.55, 84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34 (68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry eyes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.246\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRampant tooth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e47 (94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArthrodynia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC(1*109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.45 (3.6, 7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.7 (4.12, 7.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRBC(1*1012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.91\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHb(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e118.5 (108, 131.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116 (106, 127.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.474\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT(1*109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182.56\u0026thinsp;\u0026plusmn;\u0026thinsp;101.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e233.5\u0026thinsp;\u0026plusmn;\u0026thinsp;103.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESR(mm/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.82 (23, 54.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.82 (42.2, 52.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP(mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.28 (2.06, 16.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.54 (2.21, 15.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.834\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkaline phosphatase(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (61.75, 89.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.5 (72.75, 140.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgG(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77 (0.7, 0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.06 (1, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgA(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.85 (14.25, 20.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.17 (17.58, 26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgM(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02 (2.29, 3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.02 (2.65, 4.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC3(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64 (1, 1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29 (1.05, 1.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC4(g/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.17, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2 (0.17, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eANA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-SSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-RO52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-SSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eThe variables mentioned were all collected at baseline.\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\u003eNomogram development\u003c/p\u003e\n\u003cp\u003eSignificant baseline data were subjected to a univariate logistic analysis, highlighting BMI, thyroid diseases, duration of symptoms, subjective dry mouth, platelet count, alkaline phosphatase, RF, and SSB antibodies as statistically meaningful risk factors (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). To address selection bias due to the high prevalence of thrombocytopenia in hospitalized pSS patients, platelet count was excluded. Subsequently, a multivariate logistic analysis was conducted to further evaluate the significant risk factors identified in the univariate analysis. The findings confirmed the statistical significance of thyroid diseases, symptom duration, subjective dry mouth, and RF in the multivariable logistic regression. Leveraging the regression coefficients from the multivariate logistic analysis, an individualized nomogram prediction model for pSS-RTA was formulated (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The nomogram assigns scores to each risk factor, and the cumulative score corresponds to the predicted risk of pSS patients developing RTA.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate logistic regression of development set.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eFactors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI(kg/m2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.607\u0026ndash;0.932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThyroid dysfunction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.255\u0026ndash;9.668\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8-55.369\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSymptom duration(month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.003\u0026ndash;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001\u0026ndash;1.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDry mouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.483\u0026ndash;19.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.464\u0026ndash;91.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT(1*109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlkaline phosphatase(U/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1-1.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.647-166.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.412\u0026ndash;263.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-SSB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.809\u0026ndash;13.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNomogram validation\u003c/p\u003e\n\u003cp\u003eThe effectiveness and calibration of the predictive model were assessed through the area under the curve (AUC) of ROC and calibration plots. Receiver operating characteristic curves (ROC) were constructed, and the AUC values were computed for both the training and validation datasets. The Hosmer-Lemeshow test resulted in a p-value of 0.657, indicating a good model fit. The AUC values for predicting combined RTA risk in the training and validation groups were 0.912 and 0.896, respectively (Fig. 2), highlighting the nomogram\u0026rsquo;s strong discriminative capacity. The calibration curves for the training and validation groups (solid lines in Fig. \u0026nbsp;3) closely followed the ideal prediction line with a slope of 1 (dashed diagonal line), indicating the model\u0026rsquo;s reliable predictive performance. Moreover, decision curve analysis demonstrated a positive net benefit in predicting RTA risk in pSS patients using the Nomogram model, underlining its clinical relevance in RTA risk prediction (Fig. 4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn primary Sj\u0026ouml;gren\u0026rsquo;s syndrome (pSS) patients, the kidneys are commonly affected. Chronic and acute tubulointerstitial nephritis are the most common manifestations, often leading to renal tubular acidosis (RTA) clinically, similar to exocrine glands, due to lymphocyte infiltration around the kidney tubules[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, pSS-RTA typically starts insidiously, presenting few noticeable symptoms apart from electrolyte imbalances, elevated creatinine, and proteinuria. Nevertheless, the complications of fractures, life-threatening muscle paralysis, and chronic kidney disease are significant [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], underscoring the importance of early identification of pSS-RTA patients for timely intervention, improved prognosis, and enhanced quality of life.\u003c/p\u003e \u003cp\u003ePrior studies have examined potential risk factors in pSS-RTA patients, encompassing demographics, clinical features, and laboratory parameters [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Nevertheless, there has been a lack of studies specifically tackling pSS-RTA and developing a model that integrates multiple laboratory findings into clinical decision-making. In this study, we constructed a prediction model for pSS-RTA using both univariate and multivariate logistic regression analyses, visually represented as a graph to aid in assessing the risk of RTA in pSS patients. Our research pinpointed clinical characteristics linked to pSS-RTA, including BMI, thyroid disease, duration of symptoms, subjective dry mouth, platelet count, alkaline phosphatase, RF, and SSB antibodies. The multifactorial logistic regression unveiled that concomitant thyroid disease, prolonged symptom duration, subjective dry mouth, and positive RF are independent risk factors for pSS-RTA. Subsequently, we devised and validated a straightforward model to predict RTA in pSS patients. Our study outcomes may help clinicians recognize these risk factors to facilitate early detection, diagnosis, and treatment of pSS-RTA patients, thus enhancing their prognosis and quality of life.\u003c/p\u003e \u003cp\u003eOur group of pSS-RTA patients has an average age of 41 years, which is approximately 10 years younger than European pSS patients with kidney involvement (average age 52 years), and similar to previously reported Indian (average age 40.19 years) and Chinese cohorts (average age 40.1 years) [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This confirms that kidney involvement in Asian populations with pSS tends to occur at a younger age compared to Western countries.\u003c/p\u003e \u003cp\u003eThe link between thyroid disease and systemic autoimmune diseases is well-known. Among 5 patients with both thyroid disease and renal tubular acidosis, 3 also had Sj\u0026ouml;gren\u0026rsquo;s syndrome, indicating a possible connection among these conditions, although the exact mechanism remains unclear [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. A multicenter study involving 4479 pSS patients found that thyroid disease (OR 1.49, 95% CI 1.04\u0026ndash;2.14) is an independent risk factor for pSS-RTA [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Our study also supports the association between thyroid disease and pSS-RTA, emphasizing the importance of screening young women with thyroid disease for autoimmune conditions, especially pSS. Similarly, diagnosed pSS patients should be screened for thyroid disease to enable early detection and intervention.\u003c/p\u003e \u003cp\u003eOur findings suggest that pSS-RTA patients experience symptoms for a longer duration than pSS patients, highlighting a diagnostic delay possibly due to some RTA patients being undiagnosed due to mild symptoms. Additionally, symptoms related to low potassium levels may lead patients to seek care in specialties such as nephrology, gastroenterology, or endocrinology, contributing to delays in pSS diagnosis. Therefore, thorough autoimmune disease screening, particularly for pSS, is recommended for young women with RTA.\u003c/p\u003e \u003cp\u003eThere is debate regarding whether there are differences in dry mouth symptoms between pSS patients with and without RTA. Jain et al. reported a lower occurrence of dry eyes in pSS-RTA patients compared to those without RTA, while the difference in dry mouth occurrence was not statistically significant [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In our study, dry mouth was more prevalent in pSS-RTA patients than in those without RTA, while the occurrence of dry eyes was similar. One possible explanation is that RTA and exocrine gland involvement share common pathogenic mechanisms and histological characteristics. Certain targets of pSS-RTA, such as carbonic anhydrase II and H\u0026thinsp;\u0026plusmn;\u0026thinsp;ATPase, are expressed in both salivary glands and renal intercalated cells [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Another explanation could be that patients with renal tubular dysfunction may develop nephrogenic diabetes insipidus, leading to polydipsia and subjective dry mouth symptoms.\u003c/p\u003e \u003cp\u003eWe also discovered a positive correlation between RF positivity and the risk of pSS patients developing RTA. This is the first report indicating a link between RF positivity and pSS-RTA. RF, one of the autoantibodies associated with pSS, is an immunoglobulin with varying isotypes and affinities first discovered over 80 years ago, although its mechanisms and pathophysiology are not fully understood [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. RF is commonly found in patients with RA, connective tissue diseases, and various infections, and occasionally in healthy individuals. Studies have shown RF positivity in 75\u0026ndash;95% of pSS patients [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Recent research has revealed RF positivity as an independent predictor of lymphoma in pSS patients, underscoring its critical role in lymphoma development [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. There is evidence suggesting that RF positivity is associated with more severe and prolonged disease in RA patients[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings collectively demonstrate the significant role of RF in the occurrence, development, and prediction of autoimmune diseases. While more research has focused on RF in the RA field, there is a need for further exploration of RF in the context of pSS to identify new therapeutic targets and predict the evolution of pSS-related complications more accurately.\u003c/p\u003e \u003cp\u003eThis study has limitations, including a relatively small number of eligible patients due to strict inclusion criteria, the retrospective nature of the study introducing potential biases, and single-center data limiting generalizability. Moreover, the lack of time-series data in the predictive model hinders accurate risk assessment for pSS patients developing RTA. Future plans involve expanding the sample size in collaboration with multiple hospitals to establish a Nomogram with additional clinical predictors and time-series data.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our Nomogram for predicting the likelihood of pSS patients developing RTA exhibits good discrimination, calibration, and clinical benefits. Utilizing this Nomogram can guide early identification of high-risk patients and prompt intervention for better outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eACR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;American College of Rheumatology\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eANA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;antinuclear antibody\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;area under the curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;body mass index\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDCA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;decision curve analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;dermatomyositis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEULAR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;European League Against Rheumatism\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;glomerulonephritis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIgA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immunoglobulin A\u003c/p\u003e\n\u003cp\u003eIgG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Immunoglobulin G\u003c/p\u003e\n\u003cp\u003eIgM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Immunoglobulin M\u003c/p\u003e\n\u003cp\u003eIN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;interstitial nephritis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003epSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;primary Sjogren\u0026rsquo;s syndrome\u003c/p\u003e\n\u003cp\u003eRA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;rheumatoid arthritis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;rheumatoid factor\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;receiver operating characteristic\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRTA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;renal tubular acidosis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSLE\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;systemic lupus erythematosus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Sjogren\u0026rsquo;s syndrome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003esSS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;secondary Sjogren\u0026rsquo;s syndrome\u003c/p\u003e\n\u003cp\u003eSSc \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; systemic sclerosis\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYanzhen Zeng participated in the study design and analysis and wrote the manuscript. Runzhi Liu and Shuyi Li participated in study design and manuscript revision. Jingwen Wei and Fei Luo wrote the main manuscript tables and figures.Yongkang Chen and Dongmei Zhou participated in manuscript revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData and material are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Xuzhou Medical University Affiliated Hospital and the committee\u0026rsquo;s reference number is XYFY2024KL-202.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMavragani, C.P. and H.M. Moutsopoulos, \u003cem\u003eThe geoepidemiology of Sj\u0026ouml;gren\u0026apos;s syndrome.\u003c/em\u003e Autoimmun Rev, 2010. \u003cstrong\u003e9\u003c/strong\u003e(5): p. 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and followup of 130 patients with primary Sj\u0026ouml;gren\u0026apos;s syndrome.\u003c/em\u003e J Rheumatol, 2008. \u003cstrong\u003e35\u003c/strong\u003e(2): p. 278-84.\u003c/li\u003e\n\u003cli\u003eAasar\u0026oslash;d, K., et al., \u003cem\u003eRenal involvement in primary Sj\u0026ouml;gren\u0026apos;s syndrome.\u003c/em\u003e Qjm, 2000. \u003cstrong\u003e93\u003c/strong\u003e(5): p. 297-304.\u003c/li\u003e\n\u003cli\u003ePertovaara, M., et al., \u003cem\u003eThe occurrence of renal involvement in primary Sj\u0026ouml;gren\u0026apos;s syndrome: a study of 78 patients.\u003c/em\u003e Rheumatology (Oxford), 1999. \u003cstrong\u003e38\u003c/strong\u003e(11): p. 1113-20.\u003c/li\u003e\n\u003cli\u003eYang, J., et al., \u003cem\u003eNomogram for predicting the survival of patients with malignant melanoma: A population analysis.\u003c/em\u003e Oncol Lett, 2019. \u003cstrong\u003e18\u003c/strong\u003e(4): p. 3591-3598.\u003c/li\u003e\n\u003cli\u003ePan, Y.X., et al., \u003cem\u003eA 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criteria for Sj\u0026ouml;gren\u0026apos;s syndrome: a revised version of the European criteria proposed by the American-European Consensus Group.\u003c/em\u003e Ann Rheum Dis, 2002. \u003cstrong\u003e61\u003c/strong\u003e(6): p. 554-8.\u003c/li\u003e\n\u003cli\u003eShiboski, C.H., et al., \u003cem\u003e2016 American College of Rheumatology/European League Against Rheumatism classification criteria for primary Sj\u0026ouml;gren\u0026apos;s syndrome: A consensus and data-driven methodology involving three international patient cohorts.\u003c/em\u003e Ann Rheum Dis, 2017. \u003cstrong\u003e76\u003c/strong\u003e(1): p. 9-16.\u003c/li\u003e\n\u003cli\u003eBossini, N., et al., \u003cem\u003eClinical and morphological features of kidney involvement in primary Sj\u0026ouml;gren\u0026apos;s syndrome.\u003c/em\u003e Nephrol Dial Transplant, 2001. \u003cstrong\u003e16\u003c/strong\u003e(12): p. 2328-36.\u003c/li\u003e\n\u003cli\u003eGoules, A.V., et al., \u003cem\u003eClinically significant renal involvement in primary Sj\u0026ouml;gren\u0026apos;s syndrome: clinical presentation and outcome.\u003c/em\u003e Arthritis Rheum, 2013. \u003cstrong\u003e65\u003c/strong\u003e(11): p. 2945-53.\u003c/li\u003e\n\u003cli\u003eChatterjee, R., et al., \u003cem\u003eRenal involvement in Sjőgren\u0026apos;s syndrome: predictors and impact on patient outcomes.\u003c/em\u003e Rheumatol Int, 2023. \u003cstrong\u003e43\u003c/strong\u003e(7): p. 1297-1306.\u003c/li\u003e\n\u003cli\u003eZhang, Y., et al., \u003cem\u003eRenal tubular acidosis and associated factors in patients with primary Sj\u0026ouml;gren\u0026apos;s syndrome: a registry-based study.\u003c/em\u003e Clin Rheumatol, 2023. \u003cstrong\u003e42\u003c/strong\u003e(2): p. 431-441.\u003c/li\u003e\n\u003cli\u003eMason, A.M. and P.L. Golding, \u003cem\u003eRenal tubular acidosis and autoimmune thyroid disease.\u003c/em\u003e Lancet, 1970. \u003cstrong\u003e2\u003c/strong\u003e(7683): p. 1104-7.\u003c/li\u003e\n\u003cli\u003eD\u0026ouml;rner, T., et al., \u003cem\u003eRheumatoid factor revisited.\u003c/em\u003e Curr Opin Rheumatol, 2004. \u003cstrong\u003e16\u003c/strong\u003e(3): p. 246-53.\u003c/li\u003e\n\u003cli\u003eLee, K.A., et al., \u003cem\u003eClinical and diagnostic significance of serum immunoglobulin A rheumatoid factor in primary Sjogren\u0026apos;s syndrome.\u003c/em\u003e Clin Oral Investig, 2019. \u003cstrong\u003e23\u003c/strong\u003e(3): p. 1415-1423.\u003c/li\u003e\n\u003cli\u003eFragkioudaki, S., C.P. Mavragani, and H.M. Moutsopoulos, \u003cem\u003ePredicting the risk for lymphoma development in Sjogren syndrome: An easy tool for clinical use.\u003c/em\u003e Medicine (Baltimore), 2016. \u003cstrong\u003e95\u003c/strong\u003e(25): p. e3766.\u003c/li\u003e\n\u003cli\u003eNocturne, G., et al., \u003cem\u003eRheumatoid Factor and Disease Activity Are Independent Predictors of Lymphoma in Primary Sj\u0026ouml;gren\u0026apos;s Syndrome.\u003c/em\u003e Arthritis Rheumatol, 2016. \u003cstrong\u003e68\u003c/strong\u003e(4): p. 977-85.\u003c/li\u003e\n\u003cli\u003eIngegnoli, F., R. Castelli, and R. Gualtierotti, \u003cem\u003eRheumatoid factors: clinical applications.\u003c/em\u003e Dis Markers, 2013. \u003cstrong\u003e35\u003c/strong\u003e(6): p. 727-34.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"primary Sjögren’s syndrome, renal tubular acidosis, nomogram, risk factors","lastPublishedDoi":"10.21203/rs.3.rs-4453751/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4453751/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo investigate the risk factors of RTA in patients with pSS and create a personalized nomogram for predicting pSS-RTA patients.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eData from 99 pSS patients who underwent inpatient treatment at our hospital from January 2012 to January 2024 were retrospectively collected and analyzed. Bootstrap resampling technique, single-factor, and multi-factor logistic regression analyses were used to explore the risk factors for pSS-RTA. A nomogram was developed based on the results of the multivariate logistic model. The model was evaluated through receiver operating characteristic curve, C-index, calibration curve, and decision curve analysis .\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA multivariate logistic regression analysis revealed that concurrent thyroid disease, long symptom duration, subjective dry mouth, and positive RF were independent risk factors for pSS-RTA patients. Based on them, a personalized nomogram predictive model was established. With a p-value of 0.657 from the Hosmer-Lemeshow test, the model demonstrated a good fit. The AUC values in the training and validation groups were 0.912 and 0.896, indicating a strong discriminative power of the nomogram. The calibration curves for the training and validation groups closely followed the diagonal line with a slope of 1, confirming the model\u0026rsquo;s reliable predictive ability. Furthermore, the decision curve analysis showed that the nomogram model had a net benefit in predicting pSS-RTA, emphasizing its clinical value.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eWe developed a nomogram to predict RTA occurrence in pSS patients, and it is believed to provide a foundation for early identification and intervention for high-risk pSS patients.\u003c/p\u003e","manuscriptTitle":"Analysis of risk factors and development of a nomogram prediction model for tubular acidosis in primary Sjogren syndrome patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-07 22:57:17","doi":"10.21203/rs.3.rs-4453751/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-13T19:44:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-01T16:54:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-28T05:48:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"225344880123242958955242461911422531884","date":"2024-06-18T23:20:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332309268745231097128644670551395847913","date":"2024-06-18T18:41:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-08T19:09:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-23T06:36:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-05-23T06:34:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Arthritis Research \u0026 Therapy","date":"2024-05-21T09:30:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"arthritis-research-and-therapy","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arrt","sideBox":"Learn more about [Arthritis Research \u0026 Therapy](http://arthritis-research.biomedcentral.com/)","snPcode":"13075","submissionUrl":"https://submission.nature.com/new-submission/13075/3","title":"Arthritis Research \u0026 Therapy","twitterHandle":"@ArthritisRes","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"dcb68423-c9f1-400c-bcc5-c8372bb0c553","owner":[],"postedDate":"June 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-26T15:59:34+00:00","versionOfRecord":{"articleIdentity":"rs-4453751","link":"https://doi.org/10.1186/s13075-024-03383-w","journal":{"identity":"arthritis-research-and-therapy","isVorOnly":false,"title":"Arthritis Research \u0026 Therapy"},"publishedOn":"2024-08-22 15:57:03","publishedOnDateReadable":"August 22nd, 2024"},"versionCreatedAt":"2024-06-07 22:57:17","video":"","vorDoi":"10.1186/s13075-024-03383-w","vorDoiUrl":"https://doi.org/10.1186/s13075-024-03383-w","workflowStages":[]},"version":"v1","identity":"rs-4453751","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4453751","identity":"rs-4453751","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}